Differentiation of lyme disease and southern tick-associated rash illness

ABSTRACT

The present disclosure provides a biosignature that distinguishes Lyme disease, including early Lyme disease, from STARI. The present disclosure also provides methods for detecting Lyme disease and STARI, as well as methods for treating subjects diagnosed with Lyme disease or STARI.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/US2018/036688, with an international filing date of Jun. 8, 2018,which PCT application claims the benefit of U.S. provisional applicationNo. 62/516,824, filed Jun. 8, 2017. The contents of the above-mentionedapplications are hereby incorporated by reference in their entirety.

GOVERNMENTAL RIGHTS

This invention was made with government support under A1100228 andA1099094, each awarded by National Institutes of Health. The governmenthas certain rights in the invention.

REFERENCE TO SEQUENCE LISTING

The instant application contains a Sequence Listing which has beensubmitted electronically in ASCII format and is hereby incorporated byreference in its entirety. Said ASCII copy, created on Apr. 27, 2021, isnamed “CSURF_065620-642266_ST25.txt”, and is 2.58 KB in size.

FIELD OF THE INVENTION

The present disclosure relates to human disease detection tools andmethods, and in particular pertains to tools and methods for detectingLyme disease and southern tick-associated rash illness (STARI), and fordistinguishing Lyme disease from STARI.

BACKGROUND OF THE INVENTION

Lyme disease is a multisystem bacterial infection that in the UnitedStates is primarily caused by infection with Borrelia burgdorferi sensustricto. Over 300,000 cases of Lyme disease are estimated to occurannually in the United States, with over 3.4 million laboratorydiagnostic tests performed each year. Symptoms associated with thisinfection include fever, chills, headache, fatigue, muscle and jointaches, and swollen lymph nodes; however, the most prominent clinicalmanifestation in the early stage is the presence of one or more erythemamigrans (EM) skin lesions. This annular, expanding erythematous skinlesion occurs at the site of the tick bite in 70 to 80% of infectedindividuals and is typically 5 cm or more in diameter. Although an EMlesion is a hallmark for Lyme disease, other types of skin lesions canbe confused with EM, including the rash of southern tick-associated rashillness (STARI).

A strict geographic segregation of Lyme disease and STARI does notexist, as there are regions where STARI and Lyme disease areco-prevalent. Clinically, the skin lesions of STARI and early Lymedisease are indistinguishable, and no laboratory tool or method existsfor the diagnosis of STARI or differentiation of STARI from Lymedisease. The only biomarkers evaluated for differential diagnosis ofearly Lyme disease and STARI have been serum antibodies to B.burgdorferi. However, these tests have poor sensitivity for early stagesof Lyme disease, and thus a lack of B. burgdorferi antibodies cannot beused as a reliable differential marker for STARI. Thus, there is a needfor diagnostic methods to differentiate between Lyme disease and STARI,and that facilitate proper treatment, patient management and diseasesurveillance.

SUMMARY OF THE INVENTION

In one aspect, the present disclosure encompasses a method for analyzinga blood sample from a subject, the method comprising: (a) deproteinizingthe blood sample to produce a metabolite extract; (b) performing liquidchromatography coupled to mass spectrometry on a sample of themetabolite extract; and (c) providing abundance values for eachmolecular feature in Table A, Table B, Table C, or Table D.

In another aspect, the present disclosure encompasses a method forclassifying a subject as having Lyme disease or STARI, the methodcomprising: (a) deproteinizing a blood sample from a subject to producea metabolite extract, wherein the subject has at least one symptom thatis associated with Lyme disease or STARI; (b) performing liquidchromatography coupled to mass spectrometry on a sample of themetabolite extract; (c) providing abundance values for each molecularfeature in Table A, Table B, Table C, or Table D; and (d) inputting theabundance values from step (c) into a classification model trained withsamples of metabolite extracts derived from suitable controls, whereinthe classification model produces a disease score and the disease scoredistinguishes subjects with Lyme disease or STARI.

In another aspect, the present disclosure encompasses a method fortreating a subject with Lyme disease, the method comprising: (a)obtaining a disease score from a test; (b) diagnosing the subject withLyme disease based on the disease score; and (c) administering atreatment to the subject with Lyme disease, wherein the test comprisesmeasuring the amount of each molecular feature in Table A, Table B,Table C, or Table D; providing abundance values for each molecularfeature measured; and inputting the abundance values into aclassification model trained with samples derived from suitablecontrols, wherein the classification model produces a disease score andthe disease score distinguishes subjects with Lyme disease from subjectswith STARI, and optionally from healthy subjects. In certain examples,the test comprises (i) deproteinizing a blood sample from a subject toproduce a metabolite extract; (ii) performing liquid chromatographycoupled to mass spectrometry on a sample of the metabolite extract;(iii) providing abundance values for each molecular feature in Table A,Table B, Table C, or Table D; and (iv) inputting the abundance valuesfrom step (iii) into a classification model trained with samples ofmetabolite extracts derived from suitable controls, wherein theclassification model produces a disease score and the disease scoredistinguishes subjects with Lyme disease. In further examples, thesubject has at least one symptom of Lyme disease. In still furtherexamples, the Lyme disease is early Lyme disease and optionally thesymptom is an EM rash.

In another aspect, the present disclosure encompasses a method fortreating a subject with STARI, the method comprising: (a) obtaining adisease score from a test; (b) diagnosing the subject with STARI basedon the disease score; and (c) administering a treatment to the subjectwith STARI, wherein the test comprises measuring the amount of eachmolecular feature in Table A, Table B, Table C, or Table D; providingabundance values for each molecular feature measured; and inputting theabundance values into a classification model trained with samplesderived from suitable controls, wherein the classification modelproduces a disease score and the disease score distinguishes subjectswith STARI. In certain examples, the test comprises (i) deproteinizing ablood sample from a subject to produce a metabolite extract; (ii)performing liquid chromatography coupled to mass spectrometry on asample of the metabolite extract; (iii) providing abundance values foreach molecular feature in Table A, Table B, Table C, or Table D; and(iv) inputting the abundance values from step (iii) into aclassification model trained with samples of metabolite extracts derivedfrom suitable controls, wherein the classification model produces adisease score and the disease score distinguishes subjects with STARIfrom subjects with Lyme disease, including early Lyme disease, andoptionally from healthy subjects. In further examples, the subject hasat least one symptom of STARI. In still further examples, the symptom isan EM or an EM-like rash.

Other aspects and iterations of the invention are described below.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a block diagram depicting a metabolic profiling process forthe identification and application of differentiating molecular features(MFs). LC-MS data from an initial Discovery-Set of early Lyme disease(EL) and STARI samples was used to identify a list of MFs that weretargeted in a second LC-MS run. The data from both LC-MS runs wascombined to form the Targeted-Discovery-Set. The MFs were then screenedfor consistency and robustness and this resulted in a final early Lymedisease-STARI biosignature of 261 MFs. This biosignature was used fordownstream pathway analysis and for classification modeling. Twotraining-data sets along with the 261 MF biosignature list were used totrain multiple classification models, random forest (RF) and leastabsolute shrinkage and selection operator (LASSO). Data from samples oftwo Test-Sets (not included for the Discovery/Training-Set data) wereblindly tested against the two-way (EL vs STARI) and three-way [EL vsSTARI vs healthy controls (HC)] classification models. The regressioncoefficients used for each MF in the LASSO two-way and three-wayclassification models are provided in Table 5 and Table 7, respectively.

FIG. 2 is a graphical depiction of pathways differentially regulated inpatients with early Lyme disease and STARI. The 122 presumptivelyidentified MFs were analyzed using MetaboAnalyst to identify perturbedpathways between early Lyme disease and STARI. The color and size ofeach circle is based on P values and pathway impact values. Pathwayswith a >0.1 impact were considered to be perturbed and differentiallyregulated between patients with early Lyme disease and STARI. There werea total of four pathways affected: 1) glycerophospholipid metabolism; 2)sphingolipid metabolism; 3) valine, leucine and isoleucine biosynthesis;and 4) phenylalanine metabolism.

FIGS. 3A-E show metabolite identification and the association with NAEand PFAM metabolism. Structural identification of palmitoyl ethanolamide(FIG. 3A and FIG. 3B) and other NAEs in the 261 MF biosignatureindicated alteration of NAE metabolism (FIG. 3C), a pathway that caninfluence the production of PFAMs. Further MF identification revealedthat palmitamide (FIG. 3D and FIG. 3E) and other PFAMs also differed inabundance between STARI and early Lyme disease patients. Structuralidentification was achieved by retention-time alignment (FIG. 3A andFIG. 3D) of authentic standard (top panel), authentic standard spiked inpooled patient sera (middle panel), and the targeted metabolite inpooled patient sera (bottom panel), and by comparison of MS/MS spectra(FIG. 3B and FIG. 3E) of the authentic standards (top) and the targetedmetabolites in patient sera (bottom). Retention-time alignments forpalmitoyl ethanolamide (FIG. 3A) and palmitamide (FIG. 3D) weregenerated with extracted ion chromatograms for m/z 300.2892 and m/z256.2632, respectively. The relationship of PFAM formation to NAEmetabolism is highlighted in pink in FIG. 3C. The * and ** representsteps for the formation of palmitoyl ethanolamide and palmitamide,respectively. PLA, phospholipase A; PLC, phospholipase C; PLD,phospholipase D; ADH, alcohol dehydrogenase; PAM, peptidylglycineα-amidating monooxygenase.

FIGS. 4A-C graphically depict comparisons of MF abundances from the Lymedisease-STARI biosignature against healthy controls. FIG. 4A: Fourteenof the metabolites with level 1 or level 2 structural identificationswere evaluated for abundance differences between early Lyme disease(green squares) and STARI (blue triangles) normalized to the metaboliteabundance in healthy controls. Included are metabolites identified forNAE and PFAM metabolism. GP-NAE: glycerophospho-N-palmitoylethanolamine; Lyso PA (20:4): arachidonoyl lysophosphatidic acid; CMPF:3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid. The relative meanabundance and 95% confidence intervals are shown for each metabolite.FIG. 4B: Abundance fold change ranges (x-axis) plotted against thepercent of MFs from the 261 MF early Lyme disease-STARI biosignaturethat have increased (light blue) or decreased (dark blue) abundances inSTARI relative to healthy controls, and increased (light green) ordecreased (dark green) abundances in early Lyme disease relative tohealthy controls. FIG. 4C: The percent overlap of MFs between STARI andearly Lyme disease that increase (dark purple) or decrease (lightpurple) relative to healthy controls within each abundance fold changerange. An overlap of 30%, 16%, 5%, 0%, 0%, and 4% was found for MFs withincreased abundance relative to healthy controls, and 12%, 13%, 0%, 7%,0%, and 8% for MFs with decreased abundance relative to healthy controlsfor the MFs falling within the 1.0-1.4, 1.5-1.9, 2.0-2.4, 2.5-2.9,3.0-3.4, and abundance fold ranges, respectively.

FIGS. 5A-C graphically depict evaluations of the performance ofclassification models' described in the Example 1. FIG. 5A: LASSO scores(Xβ; i.e. the linear portion of the regression model) were calculatedfor Test-Set data of early Lyme disease and STARI serum samples bymultiplying the transformed abundances of the 38 MFs identified in thetwo-way LASSO model by the LASSO coefficients of the model and summingfor each sample. Scores are plotted along the y-axis; serum samples areplotted randomly along the x-axis for easier viewing. FIG. 5B: An ROCcurve demonstrates the level of discrimination that is achieved betweenearly Lyme disease and STARI using the 38 MFs of the two-way LASSOclassification model by depicting a true positive rate (sensitivity;early Lyme disease) versus a false positive rate (specificity; STARI)for the Test-Set samples (Table 7). The AUC was calculated to be 0.986.The diagonal line represents an AUC value of 0.5. The performance oftwo-tiered testing (red dot) on the same sample set (Test-Set 1) wasincluded as a reference for the sensitivity and specificity of thecurrent clinical laboratory test for Lyme disease. FIG. 5C: LASSO scores(Xβi) were calculated for the Test-Set data of early Lyme disease (greenspheres), STARI (blue spheres), and healthy control (black spheres)serum samples by multiplying the transformed abundances of the 82 MFsidentified in the three-way LASSO model by each of three LASSOcoefficients used in the model. Each axis represents the sample score inthe direction of one of the three sample groups. Scores are used incalculation of probabilities of class membership, with highestprobability determining the predicted class. Although there is overlap,the three groups predominantly occupy distinct areas of the plot.

FIGS. 6A-B graphically depict evaluations of intra- and inter-groupvariability in healthy controls (FIG. 6A) and STARI subjects (FIG. 6B).Linear discriminant analysis was performed using the 82 MFs picked byLASSO in the three-way classification model to assess the intra-groupvariability based on the geographical region or laboratory from whichhealthy control (CO-black, solid; FL-light gray, dotted; and NY-darkgray, dashed) and STARI (MO-dark blue, solid; NC-light blue, dotted; andOther-black, dashed) sera were obtained. Only slight intra-groupvariation was observed. This analysis also compared and showed cleardifferentiation of all healthy control from STARI samples regardless ofgeographical region or laboratory origin. Healthy controls from FL wereincluded in this analysis to demonstrate that healthy controls from anarea with low incidence of Lyme disease and where STARI cases occur donot differ from the healthy controls obtained from other regions andused in the classification modeling.

FIGS. 7A-B show data from level 1 identification of stearoylethanolamide. Confirmation of the structural identity of stearoylethanolamide was achieved by retention-time alignment (FIG. 7A) ofauthentic standard (top panel), authentic standard spiked in pooledpatient sera (middle panel), and the targeted metabolite in pooledpatient sera; and by comparison of MS/MS spectra (FIG. 7B) of theauthentic standard (top) and the targeted metabolite in pooled patientsera (bottom). Retention-time alignments for stearoyl ethanolamide (FIG.7A) were generated with extracted ion chromatograms for m/z 328.3204.MS/MS spectra for stearoyl ethanolamide were obtained with a collisionenergy of 20 eV.

FIGS. 8A-B show data from level 1 identification of pentadecanoylethanolamide. Confirmation of the structural identity of pentadecanoylethanolamide was achieved by retention-time alignment (FIG. 8A) ofauthentic standard (top panel), authentic standard spiked in pooledpatient sera (middle panel), and the targeted metabolite in pooledpatient sera; and by comparison of MS/MS spectra (FIG. 8B) of theauthentic standard (top) and the targeted metabolite in pooled patientsera (bottom). Retention-time alignments for pentadecanoyl ethanolamide(FIG. 8A) were generated with extracted ion chromatograms for m/z286.2737. MS/MS spectra for pentadecanoyl ethanolamide were obtainedwith a collision energy of 20 eV.

FIGS. 9A-B show data from level 1 identification of eicosanoylethanolamide. Confirmation of the structural identity of eicosanoylethanolamide was achieved by retention-time alignment (FIG. 9A) ofauthentic standard (top panel), authentic standard spiked in pooledpatient sera (middle panel), and the targeted metabolite in pooledpatient sera; and by comparison of MS/MS spectra (FIG. 9B) of theauthentic standard (top) and the targeted metabolite in pooled patientsera (bottom). Retention-time alignments for eicosanoyl ethanolamide(FIG. 9A) were generated with extracted ion chromatograms for m/z356.3517. MS/MS spectra for eicosanoyl ethanolamide were obtained with acollision energy of 20 eV.

FIGS. 10A-B show data from level 1 identification ofglycerophospho-N-palmitoyl ethanolamine. Confirmation of the structuralidentity of glycerophospho-N-palmitoyl ethanolamine was achieved byretention-time alignment (FIG. 10A) of authentic standard (top panel),authentic standard spiked in pooled patient sera (middle panel), and thetargeted metabolite in pooled patient sera; and by comparison of MS/MSspectra (FIG. 10B) of the authentic standard (top) and the targetedmetabolite in pooled patient sera (bottom). Retention-time alignmentsfor glycerophospho-N-palmitoyl ethanolamine (FIG. 10A) were generatedwith extracted ion chromatograms for m/z 454.2923. MS/MS spectra forglycerophospho-N-palmitoyl ethanolamine were obtained with a collisionenergy of 20 eV.

FIGS. 11A-B show data from level 1 identification of stearamide.Confirmation of the structural identity of stearamide was achieved byretention-time alignment (FIG. 11A) of authentic standard (top panel),authentic standard spiked in pooled patient sera (middle panel), and thetargeted metabolite in pooled patient sera; and by comparison of MS/MSspectra (FIG. 11B) of the authentic standard (top) and the targetedmetabolite in pooled patient sera (bottom). Retention-time alignmentsfor stearamide (FIG. 11A) were generated with extracted ionchromatograms for m/z 284.2943. MS/MS spectra for stearamide wereobtained with a collision energy of 20 eV.

FIGS. 12A-B show data from level 1 identification of erucamide.Confirmation of the structural identity of erucamide was achieved byretention-time alignment (FIG. 12A) of authentic standard (top panel),authentic standard spiked in pooled patient sera (middle panel), and thetargeted metabolite in pooled patient sera; and by comparison of MS/MSspectra (FIG. 12B) of the authentic standard (top) and the targetedmetabolite in pooled patient sera (bottom). Retention-time alignmentsfor erucamide (FIG. 12A) were generated with extracted ion chromatogramsfor m/z 338.3430. MS/MS spectra for erucamide were obtained with acollision energy of 20 eV.

FIGS. 13A-B show data from level 1 identification of L-phenylalanine.Confirmation of the structural identity of L-phenylalanine was achievedby retention-time alignment (FIG. 13A) of authentic standard (toppanel), authentic standard spiked in pooled patient sera (middle panel),and the targeted metabolite in pooled patient sera; and by comparison ofMS/MS spectra (FIG. 13B) of the authentic standard (top) and thetargeted metabolite in pooled patient sera (bottom). Retention-timealignments for L-phenylalanine (FIG. 13A) were generated with extractedion chromatograms for m/z 166.0852. MS/MS spectra for L-phenylalaninewere obtained with a collision energy of 20 eV.

FIGS. 14A-B show data from level 1 identification of nonanedioic acid.Confirmation of the structural identity of nonanedioic acid was achievedby retention-time alignment (FIG. 14A) of authentic standard (toppanel), authentic standard spiked in pooled patient sera (middle panel),and the targeted metabolite in pooled patient sera; and by comparison ofMS/MS spectra (FIG. 14B) of the authentic standard (top) and thetargeted metabolite in pooled patient sera (bottom). Retention-timealignments for nonanedioic acid (FIG. 14A) were generated with extractedion chromatograms for m/z 189.1122. MS/MS spectra for nonanedioic acidwere obtained with a collision energy of 10 eV.

FIGS. 15A-B show data from level 1 identification of glycocholic acid.Confirmation of the structural identity of glycocholic acid was achievedby retention-time alignment (FIG. 15A) of authentic standard (toppanel), authentic standard spiked in pooled patient sera (middle panel),and the targeted metabolite in pooled patient sera; and by comparison ofMS/MS spectra (FIG. 15B) of the authentic standard (top) and thetargeted metabolite in pooled patient sera (bottom). Retention-timealignments for glycocholic acid (FIG. 15A) were generated with extractedion chromatograms for m/z 466.3152. MS/MS spectra for glycocholic acidwere obtained with a collision energy of 20 eV.

FIGS. 16A-B show data from level 1 identification of3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF). Confirmationof the structural identity of CMPF was achieved by retention-timealignment (FIG. 16A) of authentic standard (top panel), authenticstandard spiked in pooled patient sera (middle panel), and the targetedmetabolite in pooled patient sera; and by comparison of MS/MS spectra(FIG. 16B) of the authentic standard (top) and the targeted metabolitein pooled patient sera (bottom). Retention-time alignments for CMPF(FIG. 16A) were generated with extracted ion chromatograms for m/z241.1069. MS/MS spectra for CMPF were obtained with a collision energyof 20 eV.

FIG. 17 shows data from level 2 identification of Lyso PA (20:4) byMS/MS spectral matching. The MS/MS fragmentation pattern for m/z459.2502 (**) in pooled sera at RT 19.02 is shown. A match to thefragmentation of arachidonoyl lysophosphatidic acid (Lyso PA (20:4)) inthe Metlin database is indicated by (*). MS/MS spectra for m/z 459.2502were obtained with a collision energy of 20 eV.

FIG. 18 shows data from level 2 identification of 3-ketosphingosine byMS/MS spectral matching. The MS/MS fragmentation pattern for m/z298.2740 (**) in pooled sera at RT 16.44 is shown. A match to thefragmentation of 3-ketosphingosine in the Metlin database is indicatedby (*). MS/MS spectra for m/z 298.2740 were obtained with a collisionenergy of 20 eV.

DETAILED DESCRIPTION

Lyme disease is an illness caused by a Borrelia species (e.g., Borreliaburgdorferi, Borrelia garinii, Borellia afzelii, etc.) and istransmitted to humans through the bite of infected blacklegged ticks(Ixodes species). Lyme disease can go through several stages and maycause different symptoms, depending on how long a subject has beeninfected and where in the body the infection has spread. The stages ofLyme disease include Stage 1, Stage 2, and Stage 3. Stage 1 Lyme diseasemay also be referred to as “early localized Lyme disease” or “early Lymedisease” and usually develops about 1 day to about 4 weeks afterinfection. Non-limiting examples of symptoms of Stage 1 Lyme diseaseinclude erythema migrans and flu-like symptoms, such as lack of energy,headache, stiff neck, fever, chills, muscle pain, joint pain, andswollen lymph nodes. Stage 1 Lyme disease may result in one or more thanone symptom. In some cases, Stage 1 Lyme disease does not result in anysymptoms. Stage 2 Lyme disease may also be referred to as “earlydisseminated infection” and usually develops about 1 month to about 4months after infection. Non-limiting examples of symptoms of Stage 2Lyme disease include an erythema migrans (or additional erythema migransrash sites), pain, weakness, numbness in the arms and/or legs, Bell'spalsy (facial drooping), headaches, fainting, poor memory, reducedability to concentrate, conjunctivitis, episodes of pain, redness andswelling in one or more large joints, rapid heartbeats (palpitations),and serious heart problems. Stage 3 Lyme disease may also be referred toas “late persistent Lyme disease” and usually develops months to yearsafter infection. Non-limiting examples of symptoms of Stage 3 Lymedisease include arthritis, numbness and tingling in the hands, numbnessand tingling in the feet, numbness and tingling in the back, tiredness,Bell's palsy (facial drooping), problems with memory, mood, sleepspeaking, and heart problems (pericarditis). A subject diagnosed withLyme disease, or suspected of having Lyme disease, may be identified onthe basis of one or more symptoms, geographic location, and possibilityof tick bite. Currently, several routine diagnostic tests are known fordiagnosing Lyme disease. Typically these tests detect and/or quantifyantibodies to one or more Borellia antigens, and are performed usingcommon immunoassay methods such as enzyme-linked immunoassays (EIA orELISA), immunofluorescence assays, or Western immunoblots. Generally,these tests are most reliable only a few weeks after an infection.Positive PCR and/or positive culture may also be used. (See, e.g., Mooreet al., “Current Guidelines, Common Clinical Pitfalls, and FutureDirections for Laboratory Diagnosis of Lyme Disease, United States,”Emerg Infect Dis. 2016, Vol. 22, No. 7). In one example, diagnostictesting may comprise a commercially-available C6 EIA. The C6 Lyme EIAmeasures antibody reactivity to a synthetic peptide corresponding to thesixth invariable region of VIsE, a highly conserved surface protein ofthe causative Borrelia burgdorferi bacterium. Alternatively, or inaddition, diagnostic testing may comprise using IgM and/or IgGimmunoblots following a positive or equivocal first-tier assay. As usedherein, a subject that is negative for antibodies to Lyme diseasecausing Borrelia species need only be negative by one method of testing.

Southern tick-associated rash illness (STARI) is an illness associatedwith a bite from the lone star tick, Amblyomma americanum. The causativeagent of STARI is unknown. The rash of STARI is a red, expanding“bull's-eye” lesion that develops around the site of a lone star tickbite. The rash of STARI may be referred to as an EM rash or an EM-likerash. The rash usually appears within 7 days of tick bite and expands toa diameter of 8 centimeters (3 inches) or more. Non-limiting examples ofadditional symptoms associated with STARI include discomfort and/oritching at the bite site, muscle pain, joint pain, fatigue, fever,chills, and headache. A subject diagnosed with STARI, or suspected ofhaving STARI, may be identified on the basis of one or more symptom,geographic location, and possibility of tick bite.

Complicating the clinical differentiation between Lyme disease, and inparticular early Lyme disease, and STARI are shared symptoms (forexample, an EM or EM-like rash), co-prevalence of STARI and Lyme diseasein certain geographic regions, and poor sensitivity of common diagnosticmethods for early stages Lyme disease. The present disclosure provides abiosignature that identifies Lyme disease and southern tick-associatedrash illness (STARI), and distinguishes one from the other. Variousaspects of the biosignature and its use are described in detail below.

I. Definitions

So that the present disclosure may be more readily understood, certainterms are first defined. Unless defined otherwise, all technical andscientific terms used herein have the same meaning as commonlyunderstood by one of ordinary skill in the art to which examples of thedisclosure pertain. Many methods and materials similar, modified, orequivalent to those described herein can be used in the practice of theexamples of the present disclosure without undue experimentation, thepreferred materials and methods are described herein. In describing andclaiming the examples of the present disclosure, the followingterminology will be used in accordance with the definitions set outbelow.

The term “about,” as used herein, refers to variation of in thenumerical quantity that can occur, for example, through typicalmeasuring techniques and equipment, with respect to any quantifiablevariable, including, but not limited to, mass, volume, time, distance,wave length, frequency, voltage, current, and electromagnetic field.Further, given solid and liquid handling procedures used in the realworld, there is certain inadvertent error and variation that is likelythrough differences in the manufacture, source, or purity of theingredients used to make the compositions or carry out the methods andthe like. The term “about” also encompasses these variations, which canbe up to ±5-10%, but can also be ±9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%,etc. Whether or not modified by the term “about,” the claims includeequivalents to the quantities.

As used herein, the term “accuracy” refers to the ability of a test(e.g., a diagnostic test, a classification model, etc.) to correctlydifferentiate one type of subject (e.g., a subject with Lyme disease)from one or more different types of subjects (e.g., a subject withSTARI, a healthy subject, etc.). Accuracy is equal to (true positiveresult)+(true negative result)/(true positive result)+(true negativeresult)+(false positive result)+(false negative result).

The term “biosignature” refers to a plurality of molecular featuresforming a distinctive pattern which is indicative of a disease orcondition of an animal, preferably a human.

The term “molecular feature” refers to a small molecule metabolite in ablood sample that has a mass less than 3000 Da. The term “abundancevalue” refers to an amount of a molecular feature in a blood sample. Theabundance value for a molecular feature may be identified via anysuitable method known in the art. Molecular features are defined hereinby a positive ion m/z charge ratio ±a suitable tolerance to account forinstrument variability (e.g., ±15 ppm) and optionally one or moreadditional characteristic such as retention time or a chemical structurebased on accurate mass; and abundance values for each molecular featureare obtained from a measurement of the area under the peak for themonoisotopic mass of each molecular feature determined by massspectrometry. Given that the present disclosure identifies the molecularfeatures (i.e., small molecule metabolites) forming each biosignature,alternative methods for measuring the amount of the metabolites may beused without departing from the scope of the invention.

As used herein, the term “ROC” means “receiver operatingcharacteristic”. A ROC analysis may be used to evaluate the diagnosticperformance, or predictive ability, of a test or a method of analysis. AROC graph is a plot of sensitivity and specificity of a test at variousthresholds or cut-off values. Each point on a ROC curve represents thesensitivity and its respective specificity. A threshold value can beselected based on an ROC curve, wherein the threshold value is a pointwhere sensitivity and specificity both have acceptable values. Thethreshold value can be used in applying the test for diagnosticpurposes. It will be understood that if only specificity is optimized,then the test will be less likely to generate a false positive(diagnosis of the disease in more subjects who do not have the disease)at the cost of an increased likelihood that some cases of disease willnot be identified (e.g., false negatives). If only sensitivity isoptimized, the test will be more likely to identify most or all of thesubjects with the disease, but will also diagnose the disease in moresubjects who do not have the disease (e.g., false positives). A user isable to modify the parameters, and therefore select an ROC thresholdvalue suitable for a given clinical situation, in ways that will bereadily understood by those skilled in the art.

Another useful feature of the ROC curve is an area under the curve (AUC)value, which quantifies the overall ability of the test to discriminatebetween different sample properties, for example, to discriminatebetween subjects with Lyme disease and those STARI; to discriminatebetween subjects with STARI and healthy subjects; subjects or todiscriminate between subjects with Lyme disease, STARI, and healthysubjects. A test that is no better at identifying true positives thanrandom chance will generate a ROC curve with an AUC of 0.5. A testhaving perfect specificity and sensitivity (i.e., generating no falsepositives and no false negatives) will have an AUC of 1.00. In reality,most tests will have an AUC somewhere between these two values.

As used herein, the term “sensitivity” refers to the percentage of trulypositive observations which is classified as such by a test, andindicates the proportion of subjects correctly identified as having agiven condition. In other words, sensitivity is equal to (true positiveresult)/[(true positive result)+(false negative result)].

As used herein, the term “specificity” refers to the percentage of trulynegative observations which is classified as such by a test, andindicates the proportion of subjects correctly identified as not havinga given condition. Specificity is equal to (true negative result)/[(truenegative result)+(false positive result).

As used herein, the term “subject” refers to a mammal, preferably ahuman. The mammals include, but are not limited to, humans, primates,livestock, rodents, and pets. A subject may be waiting for medical careor treatment, may be under medical care or treatment, or may havereceived medical care or treatment.

As used interchangeably herein, the terms “control group,” “normalgroup,” “control subject,” or “healthy subject” refer to a subject, or agroup of subjects, not previously diagnosed with the disease in questionand/or treated for the disease in question for a therapeuticallyeffective amount of time (e.g., 12 months or more).

As used herein, the term “blood sample” refers to a biological samplederived from blood, preferably peripheral (or circulating) blood. Theblood sample can be whole blood, plasma or serum.

The terms “treat,” “treating,” or “treatment” as used herein, refer tothe provision of medical care by a trained and licensed healthprofessional to a subject in need thereof. The medical care may be adiagnostic test, a therapeutic treatment, and/or a prophylactic orpreventative measure. The object of therapeutic and prophylactictreatment is to prevent or slow down (lessen) an undesired physiologicalchange or disease/disorder. Beneficial or desired clinical results oftherapeutic or prophylactic treatments include, but are not limited to,alleviation of symptoms, diminishment of extent of disease, stabilized(i.e., not worsening) state of disease, a delay or slowing of diseaseprogression, amelioration or palliation of the disease state, andremission (whether partial or total), whether detectable orundetectable. “Treatment” can also mean prolonging survival as comparedto expected survival if not receiving treatment. Alternatively, themedical care may be a recommendation for no intervention. For example,no medical intervention may be needed for diseases that areself-limiting. Those in need of treatment include those already with thedisease, condition, or disorder as well as those prone to have thedisease, condition or disorder or those in which the disease, conditionor disorder is to be prevented.

II. Biosignatures

In an aspect, the present disclosure provides a biosignature thatprovides an accuracy of detecting Lyme disease equal to or greater thanabout 80%. In another aspect, the present disclosure provides abiosignature that provides an accuracy of detecting STARI disease equalto or greater than about 80%.

A method for identifying a Lyme disease biosignature and/or a STARIbiosignature is detailed in the examples. Generally speaking, the methodcomprises: a) obtaining test blood samples and control blood samples; b)analyzing the test blood samples and control blood samples by massspectrometry to obtain abundance values for a plurality of molecularfeatures in the test blood samples and the control blood samples; and c)applying a statistical modeling technique to select for a plurality ofmolecular features that distinguish test blood samples from controlblood samples with an accuracy equal to or greater than about 80%. Testblood samples are from subjects with Lyme disease and/or STARI, eitherof which is confirmed using known diagnostic methods as described above;and control bloods samples are from subjects confirmed to be free ofLyme, STARI, or both using known diagnostic methods for each. Themolecular features that distinguish test blood samples from controlblood samples comprise the biosignature for that disease.

A blood sample may be a whole blood sample, a plasma sample, or a serumsample. Any of a variety of methods generally known in the art forcollecting a blood sample may be utilized. Generally speaking, thesample collection method preferably maintains the integrity of thesample such that abundance values for each molecular feature can beaccurately measured. A blood sample may be used “as is”, or a bloodsample may be processed to remove undesirable constituents. In preferredexamples, a blood sample is processed using standard techniques toremove high-molecular weight species, and thereby obtain an extractcomprising small molecule metabolites. This is referred to herein as“deproteinization” or a “deproteinization step.” For example, a solventor solvent mixture (e.g., methanol or the like) may be added to a bloodsample to precipitate these high-molecular weight species followed by acentrifugation step to separate the precipitate and themetabolite-containing supernatant. In another example, proteases may bethe added to a blood sample. In another example, size exclusionchromatography may be used.

Analysis using mass spectrometry, preferably high resolution massspectrometry, yields abundance measures for a plurality of molecularfeatures. The abundance value for each molecular feature may be obtainedfrom a measurement of the area under the peak for the monoisotopic massof each molecular feature. Identification and extraction of molecularfeatures involves finding and quantifying all the known and unknowncompounds/metabolites down to the lowest abundance, and extracting allrelevant spectral and chromatographic information. Algorithms areavailable to identify and extract molecular features. Such algorithmsinclude for example the Molecular Feature Extractor (MFE) by Agilent.MFE locates ions that are covariant (rise and fall together inabundance) but the analysis is not exclusively based on chromatographicpeak information. The algorithm uses the accuracy of the massmeasurements to group related ions—related by charge-state envelope,isotopic distribution, and/or the presence of adducts and dimers. Itassigns multiple species (ions) that are related to the same neutralmolecule (for example, ions representing multiple charge states oradducts of the same neutral molecule) to a single compound that isreferred to as a feature. Using this approach, the MFE algorithm canlocate multiple compounds within a single chromatographic peak. Specificparameters for MFE may include a minimum ion count of 600, an absoluteheight of 2,000 ion counts, ion species H+ and Na+, charge state maximum1, and compound ion count threshold of 2 or more ions. Once themolecular feature has been identified and extracted, the area under thepeak for the monoisotopic mass is used to determine the abundance valuefor the molecular feature. The monoisotopic mass is the sum of themasses of the atoms in a molecule using the unbound, ground-state, restmass of the principal (most abundant) isotope for each element insteadof the isotopic average mass. Monoisotopic mass is typically expressedin unified atomic mass units (u), also called daltons (Da).

A molecular feature is identified as a potential molecular feature forutilization in a biosignature of the present disclosure if it is presentin at least 50% of either the test blood samples or the control bloodsamples. For example, the molecular feature may be present in at least50%, at least 55%, at least 60%, at least 65%, at least 70%, at least75%, at least 80%, at least 85%, at least 90%, at least 95% or 100% ofeither the test blood samples or the control blood samples.Additionally, a molecular feature is identified as a potential molecularfeature for utilization in a biosignature of the present disclosure ifit is significantly different in abundance between the test bloodsamples and the control blood samples. Specifically, a molecular featureis identified as being significantly different if the difference inabundance value of the molecular feature in the test blood samplesversus the abundance value of the molecular feature in the bloodbiological samples has a p-value is less than 0.1, preferably less than0.05, less than 0.01, less than 0.005, or less than 0.001.

To increase the stringency of the biosignature, replicates of the testblood samples and control blood samples may be analyzed. For example,the test blood samples and control blood samples may be analyzed induplicate. Alternatively, the test biological samples and controlbiological samples may be analyzed in triplicate. Additionally, the testblood samples and control blood samples may be analyzed four, five orsix times. The replicate analysis is used to down-select the pluralityof molecular features. The down-selection results in a biosignature withincreased stringency.

Once a plurality of potential molecular features has been generated, astatistical modeling technique may be applied to select for themolecular features that provide an accuracy of disease detection that isclinically meaningful. Several statistical models are available toselect the molecular features that comprise a biosignature of thepresent disclosure. Non-limiting examples of statistical modelingtechniques include LDA, classification tree (CT) analysis, randomforests, and LASSO (least absolute shrinkage and selection operator)logistic regression analysis. Various methods are known in the art fordetermining an optimal cut-off that maximizes sensitivity and/orspecificity to serve as a threshold for discriminating samples obtainedfrom subjects with Lyme disease or STARI. For the biosignatures in TableA, Table C, and Table D, the cut-off is determined by a data point ofthe highest specificity at the highest sensitivity on the ROC curve.However, the cut-off can be set as required by situationalcircumstances. For example, in certain clinical situations it may bedesirable to minimize false-positive rates. These clinical situationsmay include, but are not limited to, the use of an experimentaltreatment (e.g., in a clinical trial) or the use of a treatmentassociated with serious adverse events and/or a higher than averagenumber of side effects. Alternatively, it may be desirable to minimizefalse-negative rates in other clinical situations. Non-limiting examplesmay include treatment with a non-pharmacological intervention, the useof a treatment with a good risk-benefit profile, or treatment with anadditional diagnostic agent. For the biosignatures in Table A, molecularfeature stability across many samples and different LC-MS analyses wasused as the cut-off.

In one example, the present disclosure provides a biosignaturecomprising each molecular feature in Table A, wherein the molecularfeatures in the table are defined at least by their m/z ratio ±15 ppm,in some examples ±10 ppm, in some examples ±5 ppm (depending uponinstrument variability). A biosignature comprising each molecularfeature in Table A provides a 98% probability of accurately detecting asample from a subject with Lyme disease, including early Lyme disease,and an 89% probability of accurately detecting a sample from a subjectwith STARI, when discriminating between a classification of Lyme diseaseand STARI. A skilled artisan will appreciate that in certain examplesone or more molecular feature may be eliminated from the model without aclinically meaningful, negative impact on the model.

TABLE A Predicted Retention Chemical m/z Time Compound Structure (basedMetabolite MF (positive (see Predicted on accurate Class or # Name ion)Mass examples) Formula mass) Pathway  1 CSU/CDC- 166.0852 165.078 1.86C₉H₁₁NO₂ Phenylalanine Phenylalanine 001 metabolism  2 CSU/CDC- 270.3156269.3076 18.02 C₁₈H₃₉N — — 012  3 CSU/CDC- 284.3314 283.3236 18.13C₁₉H₄₁N — — 013  4 CSU/CDC- 300.6407 599.268 18.27 C₃₃H₃₇N₅O₆ Asp PheArg Tyr Peptide 014 (SEQ ID NO: 1)  5 CSU/CDC- 300.2892 299.2821 19.66C₁₈H₃₇NO₂ Palmitoyl N-acyl 019 ethanolamide ethanolamine metabolism  6CSU/CDC- 734.5079 1449.9753 17.81 — — — 039  7 CSU/CDC- 370.1837369.1757 19.7 C₁₉H₂₃N₅O₃ — — 062  8 CSU/CDC- 811.1942 810.1869 12.07C₄₂H₃₀N₆O₁₂ — — 066  9 CSU/CDC- 947.7976 946.7936 14.55 C₆₂H₁₀₆O₆TAG(59:7) Triacylglycerol 067 metabolism 10 CSU/CDC- 410.2033 409.19617.18 — — — 072 11 CSU/CDC- 1487.0005 1485.9987 18.17 — — — 075 12CSU/CDC- 137.0463 136.0378 1.37 C₄H₈O₅ Threonate Sugar 086 metabolite 13CSU/CDC- 811.7965 810.7882 12.07 — — — 107 14 CSU/CDC- 616.1776 615.169915.43 — — — 132 15 CSU/CDC- 713.4492 712.4391 19.35 C₃₈H₆₅O₁₀P PG(32:5)Glycerophospholipid 152 metabolism 16 CSU/CDC- 502.3376 484.3039 19.87C₂₇H₄₀N₄O₄ Gln Leu Pro Lys Peptide 155 (SEQ ID NO: 2) 17 CSU/CDC-415.3045 414.2978 20.19 — — — 158 18 CSU/CDC- 366.3729 365.3655 22.79 —— — 164 19 CSU/CDC- 333.1446 332.1373 12.89 C₁₂H₂₀N₄O₇ Glu Gln GlyPeptide 166 20 CSU/CDC- 241.1069 240.0996 14.7 C₁₂H₁₆O₅ 3-Carboxy-4-Fatty acid 205 methyl-5-propyl-2- metabolism furanpropanoic acid (CMPF)21 CSU/CDC- 464.1916 463.1849 13.05 C₁₆H₂₉N₇O₇S Arg Asp Cys Ala Peptide211 (SEQ ID NO: 3) 22 CSU/CDC- 1249.2045 1248.1993 15.31 — — — 212 23CSU/CDC- 1248.9178 1247.9141 15.3 — — — 213 24 CSU/CDC- 158.1539157.1466 15.36 — — — 219 25 CSU/CDC- 529.3381 528.3296 16.89 C₂₄H₄₄N₆O₇Gln Val Leu Leu Peptide 227 Gly (SEQ ID NO: 4) 26 CSU/CDC- 282.2776264.2456 20.56 C₁₈H₃₂O — — 229 27 CSU/CDC- 190.1260 189.1187 14.12C₉F₁₁₉NOS 8- 2- 235 Methylthiooctanal oxocarboxylic doxime acidmetabolism 28 CSU/CDC- 382.3675 381.3603 20.23 C₂₄H₄₇NO₂ ErucicoylN-acyl 238 ethanolamide ethanolamine metabolism 29 CSU/CDC- 477.2968476.2898 22.79 C₃₁H₄₀O₄ Lys Lys Thr Thr Peptide 244 (SEQ ID NO: 5) 30CSU/CDC- 459.3968 458.3904 19.08 — — — 248 31 CSU/CDC- 342.2635 341.256515.62 C₁₉H₃₅NO₄ — — 253 32 CSU/CDC- 529.3827 1022.6938 17.86 — — — 25433 CSU/CDC- 459.2502 458.2429 19.02 C₂₃H₃₉O₇P Lyso PA(20:4)Glycerohospholipid 258 metabolism 34 CSU/CDC- 239.0919 238.0844 11.66C₁₂H₁₄O₅ Trans-2, 3, 4- Phenylpropanoid 002 trimethoxycinnamate andpolyketide metabolism 35 CSU/CDC- 389.2174 388.2094 15.47 C₁₉H₃₂O₈Methyl Fatty acid 028 10,12,13,15- metabolism bisepidioxy-16-hydroperoxy-8E- octadecenoate 36 CSU/CDC- 285.2065 284.1991 16.02C₁₆H₂₈O₄ — — 182 37 CSU/CDC- 279.1693 278.1629 11.05 C₁₅H₂₂N₂O₃ Phe LeuDipeptide 204 38 CSU/CDC- 714.6967 1427.3824 11.76 — — — 247

In one example, the present disclosure provides a biosignaturecomprising each molecular feature in Table B, wherein the molecularfeatures in the table are defined at least by their m/z ratio ±15 ppm,in some examples ±10 ppm, in some examples ±5 ppm (depending uponinstrument variability). The biosignature comprises each molecularfeature in Table B that maintains an absolute abundance fold change of 2or greater between Lyme disease and STARI, and maintains an abundancecoefficient of variation of 0.2 or less between STARI blood samples, andmaintains an abundance coefficient of variation of 0.2 or less betweenLyme disease blood samples. A skilled artisan will appreciate that incertain examples one or more molecular features may be eliminated fromthe model without a clinically meaningful, negative impact on the model.

TABLE B Predicted Retention Chemical m/z Time Compound Structure (basedMetabolite MF (positive (see Predicted on accurate Class or # Name ion)Mass examples) Formula mass) Pathway  1 CSU/CDC- 286.1444 285.1371 16.08C₁₇H₁₉NO₃ Piperine Alkaloid 006 metabolism  2 CSU/CDC- 394.3515 376.317120.09 — — — 021  3 CSU/CDC- 284.2943 283.2872 21.15 C₁₈H₃₇NO StearamidePrimary Fatty 023 Acid Amide Metabolism  4 CSU/CDC- 482.404 481.397619.99 — — — 083  5 CSU/CDC- 137.0463 136.0378 1.37 C₄H₈O₅ ThreonateSugar 086 metabolite  6 CSU/CDC- 438.3787 420.3453 19.93 — — — 217  7CSU/CDC- 158.1539 157.1466 15.36 — — — 219  8 CSU/CDC- 464.1916 463.184913.05 C₁₆H₂₉N₇O₇S Arg Asp Cys Ala Peptide 211 (SEQ ID NO: 3)  9 CSU/CDC-441.3687 440.3614 21.26 C₃₀H₄₈O₂ 4,4-Dimethyl-14a- Sterol 240 formyl-5a-metabolism cholesta-8,24- dien-3b-ol 10 CSU/CDC- 459.3968 458.3904 19.08— — — 248 11 CSU/CDC- 459.2502 458.2429 19.02 C₂₃H₃₉O₇P Lyso PA(20:4)Glycerohospholipid 258 metabolism

In one example, the present disclosure provides a biosignaturecomprising each molecular feature in Table C, wherein the molecularfeatures in the table are defined at least by their m/z ratio ±15 ppm,in some examples ±10 ppm, in some examples ±5 ppm (depending uponinstrument variability). The biosignature comprising each molecularfeature in Table C provides an 85% probability of accurately detecting asample from a subject with Lyme disease, including early Lyme disease,an 92% probability of accurately detecting a sample from a subject withSTARI, and a 93% probability of accurately detecting a sample from ahealthy subject, when discriminating between a status (classification)of Lyme disease, STARI, and healthy. A skilled artisan will appreciatethat in certain examples one or more molecular features may beeliminated from the model without a clinically meaningful, negativeimpact on the model.

TABLE C Predicted Retention Chemical m/z Time Compound StructureMetabolite MF (positive (see Predicted (based on Class or # Name ion)Mass examples) Formula accurate mass) Pathway 1 CSU/CDC- 166.0852165.078 1.86 C₉H₁₁NO₂ Phenylalanine Phenylalanine 001 metabolism 2CSU/CDC- 270.3156 269.3076 18.02 C₁₈H₃₉N — — 012 3 CSU/CDC- 284.3314283.3236 18.13 C₁₉H₄₁N — — 013 4 CSU/CDC- 300.6407 599.268 18.27C₃₃H₃₇N₅O₆ Asp Phe Arg Tyr Peptide 014 5 CSU/CDC- 300.2892 299.282119.66 C₁₈H₃₇NO₂ Palmitoyl N-acyl 019 ethanolamide ethanolaminemetabolism 6 CSU/CDC- 734.5079 1449.9753 17.81 — — — 039 7 CSU/CDC-370.1837 369.1757 19.7 C₁₉H₂₃N₅O₃ — — 062 8 CSU/CDC- 811.1942 810.186912.07 C₄₂H₃₀N₆O₁₂ — — 066 9 CSU/CDC- 947.7976 946.7936 14.55 C₆₂H₁₀₆O₆TAG(59:7) Triacylglycerol 067 metabolism 10 CSU/CDC- 410.2033 409.19617.18 — — — 072 11 CSU/CDC- 1487.0005 1485.9987 18.17 — — — 075 12CSU/CDC- 137.0463 136.0378 1.37 C₄H₈O₅ Threonate Sugar 086 metabolite 13CSU/CDC- 811.7965 810.7882 12.07 — — — 107 14 CSU/CDC- 616.1776 615.169915.43 — — — 132 15 CSU/CDC- 713.4492 712.4391 19.35 C₃₈H₆₅O₁₀P PG(32:5)Glycerophospholipid 152 metabolism 16 CSU/CDC- 502.3376 484.3039 19.87C₂₇H₄₀N₄O₄ Gln Leu Pro Lys Peptide 155 17 CSU/CDC- 415.3045 414.297820.19 — — — 158 18 CSU/CDC- 366.3729 365.3655 22.79 — — — 164 19CSU/CDC- 333.1446 332.1373 12.89 C₁₂H₂₀N₄O₇ Glu Gln Gly Peptide 166 20CSU/CDC- 241.1069 240.0996 14.7 C₁₂H₁₆O₅ 3-Carboxy-4- Fatty acid 205methyl-5-propyl- metabolism 2-furanpropanoic acid (CMPF) 21 CSU/CDC-464.1916 463.1849 13.05 C₁₆H₂₉N₇O₇S Arg Asp Cys Ala Peptide 211 22CSU/CDC- 1249.2045 1248.1993 15.31 — — — 212 23 CSU/CDC- 1248.91781247.9141 15.3 — — — 213 24 CSU/CDC- 158.1539 157.1466 15.36 — — — 21925 CSU/CDC- 529.3381 528.3296 16.89 C₂₄H₄₄N₆O₇ Gln Val Leu Leu Peptide227 Gly 26 CSU/CDC- 282.2776 264.2456 20.56 C₁₈H₃₂O — — 229 27 CSU/CDC-190.1260 189.1187 14.12 C₉H₁₉NOS 8- 2- 235 Methylthio- oxocarboxylicoctanaldoxime acid metabolism 28 CSU/CDC- 382.3675 381.3603 20.23C₂₄H₄₇NO₂ Erucicoyl N-acyl 238 ethanolamide ethanolamine metabolism 29CSU/CDC- 477.2968 476.2898 22.79 C₃₁H₄₀O₄ Lys Lys Thr Thr Peptide 244 30CSU/CDC- 459.3968 458.3904 19.08 — — — 248 31 CSU/CDC- 342.2635 341.256515.62 C₁₉H₃₅NO₄ — — 253 32 CSU/CDC- 529.3827 1022.6938 17.86 — — — 25433 CSU/CDC- 459.2502 458.2429 19.02 C₂₃H₃₉O₇P Lyso PA(20:4)Glycerohospholipid 258 metabolism 34 CSU/CDC- 886.4296 1770.8438 12.18 —— — 003 35 CSU/CDC- 181.0859 180.0788 14.7 C₁₀H₁₂O₃ 5′-(3′-Methoxy-4′-Endogenous 004 hydroxyphenyl)- metabolite gamma- associatedvalerolactone with microbiome 36 CSU/CDC- 286.1444 285.1371 16.08C₁₇H₁₉NO₃ Piperine Alkaloid 006 metabolism 37 CSU/CDC- 463.2339 462.224816.36 C₂₅H₃₄O₈ Ala Lys Met Asn Peptide 008 38 CSU/CDC- 242.2844 241.277217.1 C₁₆H₃₅N — — 009 39 CSU/CDC- 590.4237 589.4194 19.24 — — — 017 40CSU/CDC- 553.3904 552.3819 23.38 C₃₅H₅₂O₅ Furohyperforin Endogenous 026metabolite - derived from food 41 CSU/CDC- 399.2364 398.2313 16.23 — — —030 42 CSU/CDC- 580.4144 1158.8173 18.26 — — — 042 43 CSU/CDC- 704.49851372.925 18.7 — — — 052 44 CSU/CDC- 623.4521 1210.8362 19.55 — — — 06145 CSU/CDC- 389.2178 388.2099 15.52 C₁₉H₃₂O₈ — — 070 46 CSU/CDC-1111.6690 1110.6656 17.89 — — — 074 47 CSU/CDC- 482.4040 481.3976 19.99— — — 083 48 CSU/CDC- 533.1929 532.1854 20.84 C₂₃H₂₈N₆O₉ Asp His Phe AspPeptide 084 49 CSU/CDC- 466.3152 465.3085 14.73 C₂₆H₄₃NO₆ Glycocholicacid Bile acid 087 metabolism 50 CSU/CDC- 683.4728 1347.9062 17.56 — — —091 51 CSU/CDC- 227.0897 204.1002 9.68 C₉H₁₆O₅ — — 095 52 CSU/CDC-183.1016 182.0943 10.89 C₁₀H₁₄O₃ — — 098 53 CSU/CDC- 476.3055 475.299311.09 C₂₆H₄₁N₃O₅ — — 099 54 CSU/CDC- 215.1283 214.1209 12.32 C₁₁H₁₈O₄alpha-Carboxy- Endogenous 112 delta- metabolite - decalactone derivedfrom food 55 CSU/CDC- 519.1881 518.1813 12.33 C₂₀H₃₀N₄O₁₂ Poly-g-D- PolyD- 115 glutamate glutamate metabolism 56 CSU/CDC- 1086.1800 2170.343515.38 — — — 128 57 CSU/CDC- 285.2061 284.1993 15.99 C₁₆H₂₈O₄ — — 133 58CSU/CDC- 357.1363 356.1284 15.98 C₂₀H₂₀O₆ Xanthoxylol Endogenous 134metabolite - derived from food 59 CSU/CDC- 299.1853 298.1781 16.24C₁₆H₂₆O₅ Tetranor-PGE1 Prostaglandin 136 metabolism 60 CSU/CDC- 334.2580333.2514 16.36 — — — 137 61 CSU/CDC- 317.2317 316.2254 16.63 — — — 13862 CSU/CDC- 331.2471 330.2403 17.26 C₁₈H₃₄O₅ 11,12,13- Fatty acid 141trihydroxy-9- metabolism octadecenoic acid 63 CSU/CDC- 583.3480 582.337918.04 C₂₇H₄₆N₆O₈ Leu Lys Glu Pro Peptide 144 Pro 64 CSU/CDC- 648.4672647.4609 19.98 C₃₄H₆₆NO₈P PE(29:1) Glycerophospholipid 157 metabolism 65CSU/CDC- 445.2880 854.5087 12.48 C₄₅H₇₄O₁₅ (3b,21b)-12- Endogenous 165Oleanene- metabolite - 3,21,28-triol 28- derived from [arabinosyl- food(1−>3)-arabinosyl- (1−>3)-arabinoside] 66 CSU/CDC- 1486.7386 2971.466814.97 — — — 181 67 CSU/CDC- 668.4686 1317.8969 18.04 C₁₆H₂₈O₄ OmphalotinA Endogenous 183 metabolite - derived from food 68 CSU/CDC- 454.2924436.2587 18.1 C₂₁H₄₁O₇P Lyso-PA(18:1) Glycerophospholipid 184 metabolism69 CSU/CDC- 607.9324 606.9246 19.01 — — — 186 70 CSU/CDC- 521.4202503.3858 21.06 — — — 188 71 CSU/CDC- 176.0746 175.0667 2.31 — — — 193 72CSU/CDC- 596.9082 1191.8033 19.1 — — — 194 73 CSU/CDC- 532.5606 531.555518.38 — — — 203 74 CSU/CDC- 337.1667 336.1599 20.67 C₁₂H₂₄N₄O₇ — — 20675 CSU/CDC- 415.1634 207.0784 12.2 C₈H₉N₅O₂ 6-Amino-9H- Endogenous 210purine-9- metabolite - propanoic acid derived from food 76 CSU/CDC-364.3407 346.3068 20.72 — — — 218 77 CSU/CDC- 989.5004 1976.9858 12.03 —— — 222 78 CSU/CDC- 819.6064 1635.8239 12.06 — — — 224 79 CSU/CDC-286.2737 285.2666 19.08 C₁₇H₃₅NO₂ Pentadecanoyl N-acyl 237 ethanolamideethanolamine metabolism 80 CSU/CDC- 614.4833 613.4772 19.78 — — — 245 81CSU/CDC- 298.2740 297.2668 16.44 C₁₈H₃₅NO₂ 3-Ketospingosine Sphingolipid250 metabolism 82 CSU/CDC- 1003.7020 1002.696 18.46 — — — 252

In one example, the present disclosure provides a biosignaturecomprising each molecular feature in Table D, wherein the molecularfeatures in the table are defined at least by their m/z ratio ±15 ppm,in some examples ±10 ppm, in some examples ±5 ppm (depending uponinstrument variability). The biosignature comprising each molecularfeature in Table D provides an 97% probability of accurately detecting asample from a subject with Lyme disease, including early Lyme disease,and an 89% probability of accurately detecting a sample from a subjectwith STARI, when discriminating between a classification of Lyme diseaseand STARI. The biosignature comprising each molecular feature in Table Dprovides an 85% probability of accurately detecting a sample from asubject with Lyme disease, including early Lyme disease, a 92%probability of accurately detecting a sample from a subject with STARI,and a 93% probability of accurately a sample from a healthy subject,when discriminating between a classification of Lyme disease, STARI, andhealthy. A skilled artisan will appreciate that one or more molecularfeatures may be eliminated from the model without a clinicallymeaningful, negative impact on the model.

TABLE D Predicted Retention Chemical m/z Time Compound StructureMetabolite MF (positive (see Predicted (based on Class or # Name ion)Mass examples) Formula accurate mass) Pathway 1 CSU/CDC- 166.0852165.078 1.86 C₉H₁₁NO₂ Phenylalanine Phenylalanine 001 metabolism 2CSU/CDC- 270.3156 269.3076 18.02 C₁₈H₃₉N — — 012 3 CSU/CDC- 284.3314283.3236 18.13 C₁₉H₄₁N — — 013 4 CSU/CDC- 300.6407 599.268 18.27C₃₃H₃₇N₅O₆ Asp Phe Arg Tyr Peptide 014 5 CSU/CDC- 300.2892 299.282119.66 C₁₈H₃₇NO₂ Palmitoyl N-acyl 019 ethanolamide ethanolaminemetabolism 6 CSU/CDC- 734.5079 1449.9753 17.81 — — — 039 7 CSU/CDC-370.1837 369.1757 19.7 C₁₉H₂₃N₅O₃ — — 062 8 CSU/CDC- 811.1942 810.186912.07 C₄₂H₃₀N₆O₁₂ — — 066 9 CSU/CDC- 947.7976 946.7936 14.55 C₆₂H₁₀₆O₆TAG(59:7) Triacylglycerol 067 metabolism 10 CSU/CDC- 410.2033 409.19617.18 — — — 072 11 CSU/CDC- 1487.0005 1485.9987 18.17 — — — 075 12CSU/CDC- 137.0463 136.0378 1.37 C₄H₈O₅ Threonate Sugar 086 metabolite 13CSU/CDC- 811.7965 810.7882 12.07 — — — 107 14 CSU/CDC- 616.1776 615.169915.43 — — — 132 15 CSU/CDC- 713.4492 712.4391 19.35 C₃₈H₆₅O₁₀P PG(32:5)Glycerophospholipid 152 metabolism 16 CSU/CDC- 502.3376 484.3039 19.87C₂₇H₄₀N₄O₄ Gln Leu Pro Lys Peptide 155 17 CSU/CDC- 415.3045 414.297820.19 — — — 158 18 CSU/CDC- 366.3729 365.3655 22.79 — — — 164 19CSU/CDC- 333.1446 332.1373 12.89 C₁₂H₂₀N₄O₇ Glu Gln Gly Peptide 166 20CSU/CDC- 241.1069 240.0996 14.7 C₁₂H₁₆O₅ 3-Carboxy-4- Fatty acid 205methyl-5-propyl- metabolism 2-furanpropanoic acid (CMPF) 21 CSU/CDC-464.1916 463.1849 13.05 C₁₆H₂₉N₇O₇S Arg Asp Cys Ala Peptide 211 22CSU/CDC- 1249.2045 1248.1993 15.31 — — — 212 23 CSU/CDC- 1248.91781247.9141 15.3 — — — 213 24 CSU/CDC- 158.1539 157.1466 15.36 — — — 21925 CSU/CDC- 529.3381 528.3296 16.89 C₂₄H₄₄N₆O₇ Gln Val Leu Leu Peptide227 Gly 26 CSU/CDC- 282.2776 264.2456 20.56 C₁₈H₃₂O — — 229 27 CSU/CDC-190.1260 189.1187 14.12 C₉H₁₉NOS 8- 2- 235 Methylthio- oxocarboxylicoctanaldoxime acid metabolism 28 CSU/CDC- 382.3675 381.3603 20.23C₂₄H₄₇NO₂ Erucicoyl N-acyl 238 ethanolamide ethanolamine metabolism 29CSU/CDC- 477.2968 476.2898 22.79 C₃₁H₄₀O₄ Lys Lys Thr Thr Peptide 244 30CSU/CDC- 459.3968 458.3904 19.08 — — — 248 31 CSU/CDC- 342.2635 341.256515.62 C₁₉H₃₅NO₄ — — 253 32 CSU/CDC- 529.3827 1022.6938 17.86 — — — 25433 CSU/CDC- 459.2502 458.2429 19.02 C₂₃H₃₉O₇P Lyso PA(20:4)Glycerohospholipid 258 metabolism 34 CSU/CDC- 239.0919 238.0844 11.66C₁₂H₁₄O₅ Trans-2,3,4- Phenylpropanoid 002 trimethoxycinnamate andpolyketide metabolism 35 CSU/CDC- 389.2174 388.2094 15.47 C₁₉H₃₂O₈Methyl Fatty acid 028 10,12,13,15- metabolism bisepidioxy-16-hydroperoxy-8E- octadecenoate 36 CSU/CDC- 285.2065 284.1991 16.02C₁₆H₂₈O₄ — — 182 37 CSU/CDC- 279.1693 278.1629 11.05 C₁₅H₂₂N₂O₃ Phe LeuDipeptide 204 38 CSU/CDC- 714.6967 1427.3824 11.76 — — — 247 39 CSU/CDC-886.4296 1770.8438 12.18 — — — 003 40 CSU/CDC- 181.0859 180.0788 14.7C₁₀H₁₂O₃ 5′-(3′-Methoxy-4′- Endogenous 004 hydroxyphenyl)- metabolitegamma- associated valerolactone with microbiome 41 CSU/CDC- 286.1444285.1371 16.08 C₁₇H₁₉NO₃ Piperine Alkaloid 006 metabolism 42 CSU/CDC-463.2339 462.2248 16.36 C₂₅H₃₄O₈ Ala Lys Met Asn Peptide 008 43 CSU/CDC-242.2844 241.2772 17.1 C₁₆H₃₅N — — 009 44 CSU/CDC- 590.4237 589.419419.24 — — — 017 45 CSU/CDC- 553.3904 552.3819 23.38 C₃₅H₅₂O₅Furohyperforin Endogenous 026 metabolite - derived from food 46 CSU/CDC-399.2364 398.2313 16.23 — — — 030 47 CSU/CDC- 580.4144 1158.8173 18.26 —— — 042 48 CSU/CDC- 704.4985 1372.925 18.7 — — — 052 49 CSU/CDC-623.4521 1210.8362 19.55 — — — 061 50 CSU/CDC- 389.2178 388.2099 15.52C₁₉H₃₂O₈ — — 070 51 CSU/CDC- 1111.6690 1110.6656 17.89 — — — 074 52CSU/CDC- 482.4040 481.3976 19.99 — — — 083 53 CSU/CDC- 533.1929 532.185420.84 C₂₃H₂₈N₆O₉ Asp His Phe Asp Peptide 084 54 CSU/CDC- 466.3152465.3085 14.73 C₂₆H₄₃NO₆ Glycocholic acid Bile acid 087 metabolism 55CSU/CDC- 683.4728 1347.9062 17.56 — — — 091 56 CSU/CDC- 227.0897204.1002 9.68 C₉H₁₆O₅ — — 095 57 CSU/CDC- 183.1016 182.0943 10.89C₁₀H₁₄O₃ — — 098 58 CSU/CDC- 476.3055 475.2993 11.09 C₂₆H₄₁N₃O₅ — — 09959 CSU/CDC- 215.1283 214.1209 12.32 C₁₁H₁₈O₄ alpha-Carboxy- Endogenous112 delta- metabolite - decalactone derived from food 60 CSU/CDC-519.1881 518.1813 12.33 C₂₀H₃₀N₄O₁₂ Poly-g-D- Poly D- 115 glutamateglutamate metabolism 61 CSU/CDC- 1086.1800 2170.3435 15.38 — — — 128 62CSU/CDC- 285.2061 284.1993 15.99 C₁₆H₂₈O₄ — — 133 63 CSU/CDC- 357.1363356.1284 15.98 C₂₀H₂₀O₆ Xanthoxylol Endogenous 134 metabolite - derivedfrom food 64 CSU/CDC- 299.1853 298.1781 16.24 C₁₆H₂₆O₅ Tetranor-PGE1Prostaglandin 136 metabolism 65 CSU/CDC- 334.2580 333.2514 16.36 — — —137 66 CSU/CDC- 317.2317 316.2254 16.63 — — — 138 67 CSU/CDC- 331.2471330.2403 17.26 C₁₈H₃₄O₅ 11,12,13- Fatty acid 141 trihydroxy-9-metabolism octadecenoic acid 68 CSU/CDC- 583.3480 582.3379 18.04C₂₇H₄₆N₆O₈ Leu Lys Glu Pro Peptide 144 Pro 69 CSU/CDC- 648.4672 647.460919.98 C₃₄H₆₆NO₈P PE(29:1) Glycerophospholipid 157 metabolism 70 CSU/CDC-445.2880 854.5087 12.48 C₄₅H₇₄O₁₅ (3b,21b)-12- Endogenous 165 Oleanene-metabolite - 3,21,28-triol 28- derived from [arabinosyl- food(1−>3)-arabinosyl- (1−>3)-arabinoside] 71 CSU/CDC- 1486.7386 2971.466814.97 — — — 181 72 CSU/CDC- 668.4686 1317.8969 18.04 C₁₆H₂₈O₄ OmphalotinA Endogenous 183 metabolite - derived from food 73 CSU/CDC- 454.2924436.2587 18.1 C₂₁H₄₁O₇P Lyso-PA(18:1) Glycerophospholipid 184 metabolism74 CSU/CDC- 607.9324 606.9246 19.01 — — — 186 75 CSU/CDC- 521.4202503.3858 21.06 — — — 188 76 CSU/CDC- 176.0746 175.0667 2.31 — — — 193 77CSU/CDC- 596.9082 1191.8033 19.1 — — — 194 78 CSU/CDC- 532.5606 531.555518.38 — — — 203 79 CSU/CDC- 337.1667 336.1599 20.67 C₁₂H₂₄N₄O₇ — — 20680 CSU/CDC- 415.1634 207.0784 12.2 C₈H₉N₅O₂ 6-Amino-9H- Endogenous 210purine-9- metabolite - propanoic acid derived from food 81 CSU/CDC-364.3407 346.3068 20.72 — — — 218 82 CSU/CDC- 989.5004 1976.9858 12.03 —— — 222 83 CSU/CDC- 819.6064 1635.8239 12.06 — — — 224 84 CSU/CDC-286.2737 285.2666 19.08 C₁₇H₃₅NO₂ Pentadecanoyl N-acyl 237 ethanolamideethanolamine metabolism 85 CSU/CDC- 614.4833 613.4772 19.78 — — — 245 86CSU/CDC- 298.2740 297.2668 16.44 C₁₈H₃₅NO₂ 3-Ketospingosine Sphingolipid250 metabolism 87 CSU/CDC- 1003.7020 1002.696 18.46 — — — 252 88CSU/CDC- 223.0968 222.0895 14.69 C₁₂H₁₄O₄ — — 005 89 CSU/CDC- 286.1437285.1364 16.06 C₁₇H₁₉NO₃ — — 007 90 CSU/CDC- 1112.6727 1111.6663 17.86 —— — 010 91 CSU/CDC- 454.2923 453.2867 18.08 C₂₁H₄₄NO₇P Glycerophospho-N-acyl 011 N-Palmitoyl ethanolamine Ethanolamine metabolism 92 CSU/CDC-522.3580 521.3483 18.5 C₂₆H₅₂NO₇P PC(18:1) Glycerophospholipid 015metabolism 93 CSU/CDC- 363.2192 362.2132 18.58 C₂₁H₃₀O₅ 4,5α- Sterol 016dihydrocortisone metabolism 94 CSU/CDC- 388.3939 387.3868 19.53 — — —018 95 CSU/CDC- 256.2632 255.2561 20.08 C₁₆H₃₃NO Palmitic amide PrimaryFatty 020 Acid Amide Metabolism 96 CSU/CDC- 394.3515 376.3171 20.09 — —— 021 97 CSU/CDC- 228.1955 227.1885 20.99 — — — 022 98 CSU/CDC- 284.2943283.2872 21.15 C₁₈H₃₇NO Stearamide Primary Fatty 023 Acid AmideMetabolism 99 CSU/CDC- 338.3430 337.3344 22.14 C₂₂H₄₃NO 13Z- PrimaryFatty 024 Docosenamide Acid Amide (Erucamide) Metabolism 100 CSU/CDC-689.5604 688.5504 22.52 C₃₈H₇₇N₂O₆P SM(d18:1-15:0)/ Sphingolipid 025 SM(d18:1/14:1- metabolism OH) 101 CSU/CDC- 432.2803 431.2727 10.8C₂₅H₃₇NO₅ Ala Ile Lys Thr Peptide 027 102 CSU/CDC- 385.2211 384.214715.84 C₁₆H₂₈N₆O₅ Lys His Thr Peptides 029 103 CSU/CDC- 449.3261 879.612217.07 C₄₆H₈₉NO₁₂S C22-OH Sphingolipid 031 Sulfatide metabolism 104CSU/CDC- 467.3821 444.2717 17.1 C₂₄H₄₀O₈ 2-glyceryl-6-keto-Prostaglandin 032 PGF1α metabolism 105 CSU/CDC- 836.5936 835.5845 17.15C₄₄H₈₅NO₁₁S C20 Sulfatide Sphingolipid 033 metabolism 106 CSU/CDC-792.5646 791.5581 17.17 C₄₂H₈₂NO₁₀P PS(36:0) Glycerophospholipid 034metabolism 107 CSU/CDC- 356.2802 355.2722 17.35 — — — 035 108 CSU/CDC-806.5798 805.5746 17.71 C₄₃H₈₄NO₁₀P PS(37:0) Glycerophospholipid 036metabolism 109 CSU/CDC- 762.5582 761.5482 17.79 C₄₁H₈₀NO₉P PS-O(35:1)Glycerophospholipid 037 metabolism 110 CSU/CDC- 718.5308 700.4946 17.88C₃₉H₇₃O₈P PA(36:2) Glycerophospholipid 038 metabolism 111 CSU/CDC-690.4825 1361.924 17.95 — — — 040 112 CSU/CDC- 426.1798 425.1725 18.03 —— — 041 113 CSU/CDC- 741.5154 1481.0142 18.24 C₈₃H₁₅₀O₁₇P₂ CL(74:6)Glycerophospholipid 043 metabolism 114 CSU/CDC- 864.6245 863.6166 18.17C₄₆H₈₉NO₁₁S C22 Sulfatide Sphingolipid 044 metabolism 115 CSU/CDC-558.4017 1080.7347 18.28 — — — 045 116 CSU/CDC- 719.5012 1402.9377 18.26— — — 046 117 CSU/CDC- 536.3897 1053.7382 18.36 — — — 047 118 CSU/CDC-538.8674 1058.696 18.4 — — — 048 119 CSU/CDC- 653.4619 1270.8593 18.43 —— — 049 120 CSU/CDC- 732.5450 714.5092 18.47 C₄₀H₇₅O₈P PA(37:2)Glycerophospholipid 050 metabolism 121 CSU/CDC- 748.5232 1478.0059 18.58— — — 051 122 CSU/CDC- 682.4841 1328.9008 18.77 — — — 053 123 CSU/CDC-360.3615 359.3555 18.89 — — — 054 124 CSU/CDC- 441.2412 440.2325 19.09C₂₀H₃₂N₄O₇ Pro Asp Pro Leu Peptide 055 125 CSU/CDC- 638.4554 1240.84718.92 — — — 056 126 CSU/CDC- 755.5311 1474.9941 18.94 C₈₃H₁₄₄O₁₇P₂CL(74:9) Glycerophospholipid 057 metabolism 127 CSU/CDC- 711.50231386.9417 19.09 — — — 058 128 CSU/CDC- 784.5530 1567.0908 19.27 — — —059 129 CSU/CDC- 645.4660 1271.8896 19.36 — — — 060 130 CSU/CDC-300.2886 282.2569 19.84 C₁₈H₃₄O₂ 13Z- Fatty acid 063 octadecenoicmetabolism acid 131 CSU/CDC- 309.0981 308.0913 2.06 C₁₅H₁₆O₇ — — 064 132CSU/CDC- 561.2965 1120.5778 11.7 C₅₄H₈₈O₂₄ Camellioside D Endogenous 065metabolite - derived from food 133 CSU/CDC- 1106.2625 2209.5193 14.53 —— — 068 134 CSU/CDC- 371.2070 370.1997 15.52 C₁₅H₂₆N₆O₇ His Ser LysPeptide 069 135 CSU/CDC- 443.2649 442.256 15.52 C₁₉H₃₄N₆O₆ Pro Gln AlaLys Peptide 071 136 CSU/CDC- 850.6093 849.6009 17.63 C₄₈H₈₄NO₉PPS-O(42:6) Glycerophospholipid 073 metabolism 137 CSU/CDC- 697.48961358.909 18.32 — — — 076 138 CSU/CDC- 439.8234 877.6325 18.71 — — — 077139 CSU/CDC- 567.8897 566.8818 18.73 — — — 078 140 CSU/CDC- 435.2506434.243 19 C₂₁H₃₉O₇P Lyso-PA(18:2) Glycerophospholipid 079 metabolism141 CSU/CDC- 834.6136 833.6057 18.83 C₄₅H₈₈NO₁₀P PS(39:0)Glycerophospholipid 080 metabolism 142 CSU/CDC- 534.8834 533.8771 18.82— — — 081 143 CSU/CDC- 468.8441 467.8373 19.13 — — — 082 144 CSU/CDC-312.3259 311.319 22.05 — — — 085 145 CSU/CDC- 228.1955 227.1884 15.22 —— — 088 146 CSU/CDC- 385.2211 384.2143 15.83 C₂₀H₃₂O₇ Lys His ThrPeptide 089 147 CSU/CDC- 403.2338 402.2253 15.84 C16H30N6O6 Lys Gln GlnPeptide 090 148 CSU/CDC- 675.4753 1348.9377 18.37 — — — 092 149 CSU/CDC-682.4841 1345.9257 18.76 — — — 093 150 CSU/CDC- 762.5401 1506.0367 19.36— — — 094 151 CSU/CDC- 189.1122 188.1049 12.27 C₉H₁₄O₄ Nonanedioic AcidFatty acid 177 metabolism 152 CSU/CDC- 169.0860 168.0786 9.94 C₉H₁₂O₃2,6-Dimethoxy-4- Endogenous 097 methylphenol metabolite - derived fromfood 153 CSU/CDC- 276.1263 275.1196 11.16 C₁₅H₁₇NO₄ — — 100 154 CSU/CDC-314.0672 313.06 11.56 C₁₀H₁₂N₅O₅P — — 101 155 CSU/CDC- 201.1122 200.104711.56 C₁₀H₁₆O₄ Decenedioic acid Fatty acid 102 metabolism 156 CSU/CDC-115.0391 114.0318 11.57 C₅H₆O₃ 2-Hydroxy-2,4- Phenylalanine 103pentadienoate metabolism 157 CSU/CDC- 491.1569 490.1504 11.56 C₂₄H₂₆O₁₁— — 104 158 CSU/CDC- 241.1054 218.1157 11.57 C₁₀H₁₈O₅ 3-Hydroxy- Fattyacid 105 sebacic acid metabolism 159 CSU/CDC- 105.0914 104.0841 11.57 —— 106 160 CSU/CDC- 311.1472 328.1391 12.22 C₁₈H₂₀N₂O₄ Phe Tyr Peptide108 161 CSU/CDC- 271.1543 270.1464 12.24 — — — 109 162 CSU/CDC- 169.0860168.0787 12.24 C₉H₁₂O₃ 2,6-Dimethoxy-4- Endogenous 110 methylphenolmetabolite - derived from food 163 CSU/CDC- 187.0967 186.0889 12.24C₉H₁₄O₄ — — 111 164 CSU/CDC- 475.1635 474.1547 12.25 C₂₅H₂₂N₄O₆ His CysAsp Thr Peptide 113 165 CSU/CDC- 129.0547 128.0474 12.33 C₆H₈O₃ (4E)-2-Fatty acid 114 Oxohexenoic metabolism acid 166 CSU/CDC- 125.0599124.0527 13.12 C₇H₈O₂ 4-Methylcatechol Catechol 116 metabolism 167CSU/CDC- 247.1550 246.1469 13.13 C₁₂H₂₂O₅ 3-Hydroxy- Fatty acid 117dodecanedioic metabolism acid 168 CSU/CDC- 517.2614 516.2544 13.13C₂₁H₃₆N₆O₉ Gln Glu Gln Ile Peptide 118 169 CSU/CDC- 301.0739 300.065813.14 C₁₆H₁₂O₆ Chrysoeriol Endogenous 119 metabolite - derived from food170 CSU/CDC- 327.1773 304.1885 14.17 C₁₆H₂₄N₄O₂ — — 120 171 CSU/CDC-387.2023 386.1935 14.51 C₁₉H₃₀O₈ Citroside A Endogenous 121 metabolite -derived from food 172 CSU/CDC- 875.8451 1749.684 14.55 — — — 122 173CSU/CDC- 737.5118 736.5056 14.52 C₄₂H₇₃O₈P PA(39:5) Glycerophospholipid123 metabolism 174 CSU/CDC- 1274.3497 1273.3481 14.96 — — — 124 175CSU/CDC- 1274.2092 1273.2 14.96 — — — 125 176 CSU/CDC- 1486.57282971.1328 14.95 — — — 126 177 CSU/CDC- 965.3818 964.3727 15.37 — — — 127178 CSU/CDC- 1086.0562 2170.0908 15.38 C₉₇H₁₆₇N₅O₄₈ NeuAcalpha2-Sphingolipid 129 3Galbeta1- metabolism 3GalNAcbeta1- 4(9-OAc-NeuAcalpha2- 8NeuAcalpha2- 3)Galbeta1- 4Glcbeta- Cer(d18:1/18:0) 179CSU/CDC- 1086.4344 2169.8474 15.39 — — 130 180 CSU/CDC- 1240.78001239.7712 15.38 — — — 131 181 CSU/CDC- 317.1956 316.1885 16.24C₁₂H₂₄N₆O₄ Arg Ala Ala Peptide 135 182 CSU/CDC- 299.2219 298.2148 16.64C₁₇H₃₀O₄ 8E- Fatty acid 139 Heptadecenedioic metabolism acid 183CSU/CDC- 748.5408 747.5317 17.23 C₄₀H₇₈NO₉P PS-O(34:1)Glycerophospholipid 140 metabolism 184 CSU/CDC- 712.4935 1422.9749 17.82C₇₉H₁₄0O₁₇P₂ CL(70:7) Glycerophospholipid 142 metabolism 185 CSU/CDC-674.5013 673.4957 17.99 C₃₇H₇₂NO₇P PE-P(32:1) Glycerophospholipid 143metabolism 186 CSU/CDC- 677.9537 676.9478 18.36 — — — 145 187 CSU/CDC-531.3522 530.3457 18.4 C₃₅H₄₆O₄ — — 146 188 CSU/CDC- 585.2733 584.264918.39 C₃₃H₃₆N₄O₆ 15,16- Bilirubin 147 Dihydrobiliverdin breakdownproducts - Porphyrin metabolism 189 CSU/CDC- 513.3431 512.3352 18.4 — —— 148 190 CSU/CDC- 611.9156 610.9073 18.59 — — — 149 191 CSU/CDC-549.0538 531.0181 18.38 — — — 150 192 CSU/CDC- 755.5311 1509.0457 18.93— — — 151 193 CSU/CDC- 599.4146 598.4079 19.59 C₄₀H₅₄O₄ IsomytiloxanthinIsoflavinoid 153 194 CSU/CDC- 762.5029 761.4919 19.66 C₄₃H₇₂NO₈PPE(38:7) Glycerophospholipid 154 metabolism 195 CSU/CDC- 741.4805740.4698 19.96 C₄₀H₆₉O₁₀P PG(34:5) Glycerophospholipid 156 metabolism196 CSU/CDC- 516.3532 498.3199 20.27 C₂₃H₄₂N₆O₆ Ala Leu Ala Pro Peptide159 Lys 197 CSU/CDC- 769.5099 768.5018 20.53 C₄₂H₇₃O₁₀P PG(36:5)Glycerophospholipid 160 metabolism 198 CSU/CDC- 862.5881 861.5818 20.86— — — 161 199 CSU/CDC- 837.5358 836.5274 21.11 C₅₃H₇₂O₈ AmitenoneEndogenous 162 metabolite - derived from food 200 CSU/CDC- 558.3995540.367 21.44 C₂₆H₄₈N₆O₆ Leu Ala Pro Lys Peptide 163 Ile 201 CSU/CDC-1105.9305 2209.8462 14.53 — — — 167 202 CSU/CDC- 329.1049 328.0976 14.61C₁₈H₁₆O₆ 2-Oxo-3- Phenylalanine 168 phenylpropanoic metabolism acid 203CSU/CDC- 1241.2053 1240.2 15.38 — — — 169 204 CSU/CDC- 1088.67311087.6676 17.85 — — — 170 205 CSU/CDC- 667.4391 666.4323 20.35 C₃₇H₆₃O₈PPA(24:5) Glycerophospholipid 171 metabolism 206 CSU/CDC- 133.0497132.0423 11.57 C₅H₈O₄ 2-Acetolactic Pantothenate 172 acid and CoABiosynthesis Pathway 207 CSU/CDC- 259.1540 258.1469 11.75 — — — 173 208CSU/CDC- 311.1472 288.1574 12.23 C₁₀H₂₀N₆O₄ Asn Arg Dipeptide 174 209CSU/CDC- 147.0652 146.0579 12.33 C₆H₁₀O₄ α-Ketopantoic Pantothenate 175acid and CoA Biosynthesis Pathway 210 CSU/CDC- 169.0860 168.0788 12.29C₉H₁₂O₃ Epoxyoxophorone Endogenous 176 metabolite - derived from food211 CSU/CDC- 187.0965 186.0894 9.93 C₉H₁₄O₄ 5- Endogenous 096Butyltetrahydro- metabolite - 2-oxo-3- derived from furancarboxylic foodacid 212 CSU/CDC- 139.1116 138.1044 12.95 C₉H₁₄O₄ 3,6-NonadienalEndogenous 178 metabolite - derived from food 213 CSU/CDC- 515.2811514.2745 13.14 C₂₆H₄₂O₁₀ Cofaryloside Endogenous 179 metabolite -derived from food 214 CSU/CDC- 283.1522 282.1444 13.93 C₂₅H₄₂N₂O₇SEpidihydrophaseic Endogenous 180 acid metabolite - derived from food 215CSU/CDC- 706.9750 705.9684 18.7 — — — 185 216 CSU/CDC- 834.5575 833.550220.32 — — — 187 217 CSU/CDC- 683.4727 1364.9294 17.54 — — — 189 218CSU/CDC- 728.9890 1455.9633 18.63 — — — 190 219 CSU/CDC- 726.51041451.0035 18.64 C₈₁H₁₄4O₁₇P₂ CL(72:7) Glycerophospholipid 191 metabolism220 CSU/CDC- 633.9280 632.9206 18.47 — — — 192 221 CSU/CDC- 209.0784208.0713 9.92 C₁₇H₂₄O₃ Benzylsuccinate Phenylpropanoic 195 acidmetabolism 222 CSU/CDC- 792.5483 1566.055 18.46 — — — 196 223 CSU/CDC-618.9221 1218.8083 19.02 — — — 197 224 CSU/CDC- 549.0543 531.0189 18.37— — — 198 225 CSU/CDC- 553.7262 552.7188 18.74 — — — 199 226 CSU/CDC-756.0320 755.0266 18.95 — — — 200 227 CSU/CDC- 639.6307 638.6205 19.58 —— — 201 228 CSU/CDC- 753.4414 730.4513 19.37 C₄₂H₆₇O₈P PA(39:8)Glycerophospholipid 202 metabolism 229 CSU/CDC- 328.3204 327.3148 20.72C₂₀H₄₁NO₂ Stearoyl N-acyl 207 ethanolamide ethanolamine metabolism 230CSU/CDC- 514.3718 1009.7122 18.42 C₅₆H₉₉NO₁₄ 3-O-acetyl- Sphingolipid208 sphingosine- metabolism 2,3,4,6-tetra-O- acetyl- GalCer(d18:1/h22:0) 231 CSU/CDC- 630.4594 1241.8737 19.95 — — — 209 232 CSU/CDC-244.2270 243.22 17.17 C₁₄H₂₉NO₂ Lauroyl N-acyl 214 ethanolamideethanolamine metabolism 233 CSU/CDC- 463.3426 924.6699 18.08 — — — 215234 CSU/CDC- 468.3892 450.3553 19.17 C₃₁H₄₆O₂ — — 216 235 CSU/CDC-438.3787 420.3453 19.93 — — — 217 236 CSU/CDC- 792.0006 790.995 12.04 —— — 220 237 CSU/CDC- 792.2025 791.1947 12.04 — — — 221 238 CSU/CDC-791.6016 790.594 12.04 — — — 223 239 CSU/CDC- 1115.5593 2228.1028 14.95— — — 225 240 CSU/CDC- 1486.9176 2970.7976 14.96 — — — 226 241 CSU/CDC-430.3161 412.2845 20.23 C₂₃H₄₀O₆ — — 228 242 CSU/CDC- 297.2793 296.273420.66 C₁₉H₃₆O₂ Methyl oleate Oleic acid 230 ester 243 CSU/CDC- 714.36551426.718 11.73 — — — 231 244 CSU/CDC- 714.5306 1427.0479 11.76 — — — 232245 CSU/CDC- 989.7499 1977.4865 12.03 — — — 233 246 CSU/CDC- 221.0744220.0672 13.7 C₇H₁₂N₂O₆ L-beta-aspartyl- Peptide 234 L-serine 247CSU/CDC- 313.2734 312.2663 18.91 C₁₉H₃₆O₃ 2-oxo- Fatty acid 236nonadecanoic metabolism acid 248 CSU/CDC- 337.2712 314.282 20.66C₁₉H₃₈O₃ 2-Hydroxy- Fatty acid 239 nonadecanoic metabolism acid 249CSU/CDC- 441.3687 440.3614 21.26 C₃₀H₄₈O₂ 4,4-Dimethyl- Sterol 24014a-formyl-5a- metabolism cholesta-8,24- dien-3b-ol 250 CSU/CDC-425.3735 424.3666 21.5 C₃₀H₄₈O Butyrospermone Sterol 241 metabolism 251CSU/CDC- 356.3517 355.3448 21.67 C₂₂H₄₅NO₂ Eicosanoyl N-acyl 242ethanolamide ethanolamine metabolism 252 CSU/CDC- 393.2970 370.308222.46 C₂₂H₄₂O₄ — — 243 253 CSU/CDC- 167.9935 166.9861 13.2 C₇H₅NS₂ — —246 254 CSU/CDC- 677.6170 676.6095 20.71 C₄₇H₈₀O₂ Cholesterol esterSterol 249 (20:2) metabolism 255 CSU/CDC- 460.2695 459.2627 16.87C₂₆H₃₇NO₆ — — 251 256 CSU/CDC- 630.4765 612.4417 18.11 — — — 255 257CSU/CDC- 514.3734 1026.7281 18.41 — — — 256 258 CSU/CDC- 667.47541315.916 19.28 — — — 257 259 CSU/CDC- 516.8549 1031.6945 18.43 — — — 259260 CSU/CDC- 740.5242 1479.0334 19.4 C₈₃H₁₄₈O₁₇P₂ CL(74:7)Glycerohospholipid 260 metabolism 261 CSU/CDC- 1104.0614 2206.1096 15.2— — — 261

III. Methods for Analyzing a Blood Sample from a Subject

In another aspect, the present disclosure provides a method foranalyzing a blood sample from a subject. The method comprises performingliquid chromatography coupled to mass spectrometry on a blood sample,and providing abundance values for each molecular feature in Table A,Table B, Table C, or Table D. Preferably, the method further comprisesdeproteinizing a blood sample from a subject to produce a metaboliteextract and then performing liquid chromatography coupled to massspectrometry on a sample of the metabolite extract. The method maycomprise providing abundance values for each molecular feature in TableA or Table C. The method may comprise providing abundance values foreach molecular feature in Table B or Table D. The method may compriseproviding abundance values for each molecular feature in Table A, TableB, or Table D. The method may comprise providing abundance values foreach molecular feature in Table C or Table D.

A subject may be a human or a non-human mammal including, but notlimited to, a livestock animal, a companion animal, a lab animal, or azoological animal. A subject may be a rodent, e.g., a mouse, a rat, aguinea pig, etc. A subject may also be a livestock animal. Non-limitingexamples of suitable livestock animals may include pigs, cows, horses,goats, sheep, llamas and alpacas. Alternatively, a subject may be acompanion animal. Non-limiting examples of companion animals may includepets such as dogs, cats, rabbits, and birds. A subject may be azoological animal. As used herein, a “zoological animal” refers to ananimal that may be found in a zoo. Such animals may include non-humanprimates, large cats, wolves, and bears. In preferred examples, asubject is human.

Methods of the present disclosure for analyzing a blood sample may beused to monitor the progression or resolution of Lyme disease or STARI.A skilled artisan will also appreciate that infection with Borreliaspecies that cause Lyme disease, or with the causative agent(s) ofSTARI, likely commences prior to diagnosis or the onset of symptomsassociated with the disease. For at least these reasons, a suitableblood sample may be from a subject that may or may not have a symptomassociated with Lyme disease or STARI. Non-limiting examples of symptomsassociated with Lyme disease and STARI are described above. A subjectmay have at least one symptom associated with Lyme disease, at least onesymptom associated with STARI, or at least one symptom associated withLyme disease and STARI. As a non-limiting example, a subject can have anerythema migrans (EM) rash or an EM-like rash. Alternatively, a subjectmay not have a symptom of Lyme disease or STARI but may be at risk ofhaving Lyme disease or STARI. Non-limiting examples of risk factors forLyme disease or STARI include living in or visiting a region endemic forLyme disease or STARI, spending time in wooded or grassy areas, camping,fishing, gardening, hiking, hunting and/or picnicking in a regionendemic for Lyme disease or STARI, and not removing tick(s) promptly orproperly. In each of the above examples, suitable subjects, whether ornot they have a symptom associated with Lyme disease or STARI at thetime a blood sample is obtained, may or may not have received (or bereceiving) treatment for Lyme disease, STARI, or another disease withsymptoms similar to Lyme disease or STARI.

A blood sample may be a whole blood sample, a plasma sample, or a serumsample. Any of a variety of methods generally known in the art forcollecting a blood sample may be utilized. Generally speaking, thesample collection method preferably maintains the integrity of thesample such that abundance values for each molecular feature in Table A,Table B, Table C, or Table D can be accurately measured according to thedisclosure. A blood sample may be used “as is”, or a blood sample may beprocessed to remove undesirable constituents. In preferred examples, ablood sample is processed using standard techniques to removehigh-molecular weight species, and thereby obtain an extract comprisingsmall molecule metabolites. This is referred to herein as“deproteinization” or a “deproteinization step.” For example, a solventor solvent mixture (e.g., methanol or the like) may be added to a bloodsample to precipitate these high-molecular weight species followed by acentrifugation step to separate the precipitate and themetabolite-containing supernatant. In another example, proteases may bethe added to a blood sample. In another example, size exclusionchromatography may be used.

A single blood sample may be obtained from a subject. Alternatively, themolecular features may be detected in blood samples obtained over timefrom a subject. As such, more than one blood sample may be collectedfrom a subject over time. For instance, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16 or more blood samples may be collected from a subjectover time. For example, 2, 3, 4, 5, or 6 blood samples are collectedfrom a subject over time. Alternatively, 6, 7, 8, 9, or 10 blood samplesare collected from a subject over time. Further, 10, 11, 12, 13, or 14blood samples are collected from a subject over time. Still further, 14,15, 16 or more blood samples are collected from a subject over time. Theblood samples collected from the subject over time may be used tomonitor Lyme disease or STARI in a subject. Alternatively, the bloodsamples collected from the subject over time may be used to monitorresponse to treatment in a subject.

When more than one sample is collected from a subject over time, bloodsamples may be collected 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ormore days apart. For example, blood samples may be collected 0.5, 1, 2,3, or 4 days apart. Alternatively, blood samples may be collected 4, 5,6, or 7 days apart. Further, blood samples may be collected 7, 8, 9, or10 days apart. Still further, blood samples may be collected 10, 11, 12or more days apart.

Once a sample is obtained, it is processed in vitro to measure abundancevalues for each molecular feature in Table A, Table B, Table C, or TableD. All suitable methods for measuring the abundance value for each ofthe molecular features known to one of skill in the art are contemplatedwithin the scope of the invention. For example, mass spectrometry may beused to measure abundance values for each molecular feature in Table A,Table B, Table C, or Table D. The abundance values may be determinedthrough direct infusion into the mass spectrometer. Alternatively,techniques coupling a chromatographic step with a mass spectrometry stepmay be used. The chromatographic step may be liquid chromatography. Incertain examples, the abundance value for each of the molecular featuresmay be determined utilizing liquid chromatography followed by massspectrometry (LC-MS). In some examples, the liquid chromatography ishigh performance liquid chromatography (HPLC). Non-limiting examples ofHPLC include partition chromatography, normal phase chromatography,displacement chromatography, reversed phase chromatography, sizeexclusion chromatography, ion exchange chromatography, bioaffinitychromatography, aqueous normal phase chromatography or ultrafast liquidchromatography. As used herein “mass spectrometry” describes methods ofionization coupled with mass selectors. Non-limiting examples of methodsof ionization include matrix-assisted laser desorption/ionization(MALDI), electrospray ionization (ESI), and atmospheric pressurechemical ionization (ACPI). Non-limiting examples of mass selectorsinclude quadropole, time of flight (TOF), and ion trap. Further, themass selectors may be used in combination such as quadropole-TOF ortriple quadropole.

In one example, an aliquot of a serum metabolite extract may be appliedto a Poroshell 120, EC-C8, 2.1×100 mm, 2.7 μm LC Column (AgilentTechnologies, Palo Alto, Calif.), and metabolites may be eluted with anonlinear gradient of acetonitrile in formic acid (e.g., 0.1%) at a flowrate of 250 μl/min with an Agilent 1200 series LC system. The eluent maybe introduced directly into an Agilent 6520 quadrapole time of flightmass (Q-TOF) spectrometer and MS may be performed as previouslydescribed (27, 50). LC-MS and LC-MS/MS data may be collected under thefollowing parameters: gas temperature, 310° C.; drying gas at 10 litersper min; nebulizer at 45 lb per in²; capillary voltage, 4,000 V;fragmentation energy, 120 V; skimmer, 65 V; and octapole RF setting, 750V. The positive-ion MS data for the mass range of 75 to 1,700 Da may beacquired at a rate of 2 scans per sec in both centroid and profile modesin 4-GHz high-resolution mode. Positive-ion reference masses may be usedto ensure mass accuracy. To monitor instrument performance, qualitycontrol samples having a metabolite extract of healthy control serum maybe analyzed in duplicate at the beginning of each analysis day and every20 samples during the analysis day. In view of the specifics disclosedin this example, a skilled artisan will be able to optimize conditionsas needed when using alternative equipment or approaches.

IV. Methods for Classifying a Subject as Having Lyme Disease or STARI

In another aspect, the present disclosure provides a method forclassifying a subject as having Lyme disease or STARI. The methodcomprises analyzing a blood sample from a subject as described inSection III to provide abundance values for each molecular feature inTable A, Table B, Table C, or Table D; and comparing the abundancevalues to a reference set of abundance values. The statisticalsignificance of any difference between the abundance values measured inthe subject's blood sample as compared to the abundance values from thereference set is then determined. If the difference is statisticallysignificant then a subject may be classified as having Lyme disease orSTARI; if the difference is not statistically significant then a subjectmay be classified as not having Lyme disease or STARI. For instance,when using p-values, the abundance value of a molecular feature in atest blood sample is identified as being significantly different fromthe abundance value of the molecular feature in the reference set whenthe p-value is less than 0.1, preferably less than 0.05, less than 0.01,less than 0.005, or less than 0.001. Abundance values for the molecularfeatures from the reference set may be determined before, after, or atthe same time, as the abundance values for the molecular features fromthe subject's blood sample. Alternatively, abundance values for themolecular features from a reference set stored in a database may beused.

Any suitable reference set known in the art may be used; alternatively anew reference set may be generated. A suitable reference set comprisesthe abundance values for each of the molecular feature in Table A, TableB, Table C, or Table D in blood sample(s) obtained from control subjectsknown to be positive for Lyme disease, known to be positive for STARI,known to be negative for Lyme disease, known to be negative for STARI,known to be negative for Lyme disease and STARI, healthy subjects, orany combination thereof. Further, control subjects known to be negativefor Lyme disease and/or STARI may also be known to be suffering from adisease with overlapping symptoms, may exhibit serologiccross-reactivity with Lyme disease, and/or may be suffering for anotherspirochetal infection. A subject suffering from a disease withoverlapping symptoms may have one or more of the symptoms of Lymedisease described above. Non-limiting examples of diseases withoverlapping symptoms include tick-bite hypersensitivity reactions,certain cutaneous fungal infections and bacterial cellulitis withnon-Lyme EM-like lesions, syphilis, fibromyalgia, lupus, mixedconnective tissue disorders (MCTD), chronic fatigue syndrome (CFS),rheumatoid arthritis, depression, mononucleosis, multiple sclerosis,sarcoidosis, endocarditis, colitis, Crohn's disease, early ALS, earlyAlzheimers disease, encephalitis, Fifth's disease, gastroesophagealreflux disease, infectious arthritis, interstitial cystis, irritablebowel syndrome, juvenile arthritis, Ménières syndrome, osteoarthritis,prostatitis, psoriatic arthritis, psychiatric disorders (bipolar,depression, etc.), Raynaud's syndrome, reactive arthritis, scleroderma,Sjogren's syndrome, sleep disorders, and thyroid disease. Specifically,a disease with overlapping symptoms is selected from the groupconsisting of syphilis and fibromyalgia. Further, the disclosureprovides a method of correctly distinguishing a subject with early Lymedisease from a subject exhibiting serologic cross-reactivity with Lymedisease. A 2-tier serology-based assay is frequently used to diagnoseLyme disease. However, such an assay suffers from poor sensitivity insubjects with early Lyme disease. Non-limiting examples of diseases thatexhibit serologic cross-reactivity with Lyme disease include infectiousmononucleosis, syphilis, periodontal disease caused by Treponemadenticola, granulocytic anaplasmosis, Epstein-Barr virus infection,malaria, Helicobacter pylori infections, bacterial endocarditis,rheumatoid arthritis, multiple sclerosis, infections caused by otherspirochetes, and lupus. Specifically, a disease with serologiccross-reactivity is selected from the group consisting of infectiousmononucleosis and syphilis. Non-limiting examples of other spirochetalinfections include syphilis, severe periodontitis, leptospirosis,relapsing fever, rate-bite fever, bejel, yaws, pinta, and intestinalspirochaetosis. Specifically, another spirochetal infection is selectedfrom the group consisting of syphilis and severe periodontitis.

In one example, a method for classifying a subject as having Lymedisease comprises: (a) deproteinizing a blood sample from a subject toproduce a metabolite extract; (b) performing liquid chromatographycoupled to mass spectrometry on a sample of the metabolite extract; (c)providing abundance values for each molecular feature in Table A, TableB, Table C, or Table D; and (d) inputting the abundance values from step(c) into a classification model trained with samples of metaboliteextracts derived from suitable controls, wherein the classificationmodel produces a disease score and the disease score distinguishessubjects with Lyme disease. In one example, the subject has at least onesymptom associated with Lyme disease. In a specific example, the subjecthas an erythema migrans rash or an EM-like rash. In another example, thesubject does not have a symptom of Lyme disease but is at risk of havingLyme disease. In each of the above examples, the subject may or may nothave received (or be receiving) treatment for Lyme disease, STARI, oranother disease with symptoms similar to Lyme disease or STARI.

In another example, a method for classifying a subject as having STARIcomprises: (a) deproteinizing a blood sample from a subject to produce ametabolite extract; (b) performing liquid chromatography coupled to massspectrometry on a sample of the metabolite extract; (c) providingabundance values for each molecular feature in Table A, Table B, TableC, or Table D; and (d) inputting the abundance values from step (c) intoa classification model trained with samples of metabolite extractsderived from suitable controls, wherein the classification modelproduces a disease score and the disease score distinguishes subjectswith STARI. In one example, the subject has at least one symptomassociated with STARI. In a specific example, the subject has anerythema migrans rash or an EM-like rash. In another example, thesubject does not have a symptom of STARI but is at risk of having STARI.In each of the above examples, the subject may or may not have received(or be receiving) treatment for Lyme disease, STARI, or another diseasewith symptoms similar to Lyme disease or STARI.

In another example, a method for classifying a subject as having Lymedisease or STARI comprises: (a) deproteinizing a blood sample from asubject to produce a metabolite extract; (b) performing liquidchromatography coupled to mass spectrometry on a sample of themetabolite extract; (c) providing abundance values for each molecularfeature in Table A, Table B, Table C, or Table D; and (d) inputting theabundance values from step (c) into a classification model trained withsamples of metabolite extracts derived from suitable controls, whereinthe classification model produces a disease score and the disease scoredistinguishes subjects with Lyme disease from subjects STARI, andoptionally further distinguishes healthy subjects. In one example, thesubject has at least one symptom associated with Lyme disease and/or atleast one symptom associated with STARI. In a specific example, thesubject has an erythema migrans rash or an EM-like rash. In anotherexample, the subject does not have a symptom of Lyme disease or STARIbut is at risk of having Lyme disease or STARI. In each of the aboveexamples, the subject may or may not have received (or be receiving)treatment for Lyme disease, STARI, or another disease with symptomssimilar to Lyme disease or STARI.

In each of the above examples, the classification model has been trainedwith samples derived from suitable controls. Any suitable classificationsystem known in the art may be used, provided the model producedtherefrom has an accuracy of at least 80% for detecting a sample from asubject with Lyme disease, including early Lyme disease, and/or anaccuracy of at least 80% for detecting a sample from a subject withSTARI. For example, a classification model may have an accuracy of about80%, about 85%, about 90%, about 95%, or greater for detecting a samplefrom a subject with Lyme disease, including early Lyme disease, and/oran accuracy of about 80%, about 85%, about 90%, about 95%, or greaterfor detecting a sample from a subject with STARI. Non-limiting examplesof suitable classification models include LASSO, RF, ridge regression,elastic net, linear discriminant analysis, logistic regression, supportvector machines, CT, and kernel estimation. In various examples, themodel has a sensitivity from about 0.8 to about 1, and/or a specificityfrom about 0.8 to about 1. In certain examples, area under the ROC curvemay be used to evaluate the suitability of a model, and an AUC ROC valueof about 0.8 or greater indicates the model has a suitable accuracy.

The classification model produces a disease score and the disease scoredistinguishes: (i) samples from subjects with Lyme disease from samplesfrom subjects with STARI, or (ii) distinguishes samples from subjectswith Lyme disease from samples from control subjects, or (iii)distinguishes samples from subjects with STARI from samples from controlsubjects, or (iv) distinguishes samples from subjects with Lyme disease,samples from subjects with STARI and samples from control subjects fromone another. As a non-limiting example, LASSO scores for a subject'ssample may be calculated by multiplying the respective regressioncoefficients resulting from LASSO analysis by the transformed abundanceof each MF in the biosignature and summing for each sample. In a furtherexample, the sample score may be transformed into probabilities for eachsample being classified to each sample group. As another non-limitingexample, the transformed abundances of all MFs are used to classify thesample into one of the sample groups in each classification treedeveloped in an RF model, where the levels of chosen MFs are usedsequentially to classify the samples, and the final classification isdetermined by majority votes among all such classification trees in theRF model. Scores from alternative classification models may becalculated as is known in the art.

In one example, abundance values are provided for each molecular featurein Table A, Table B, or Table D; the suitable controls comprise a bloodsample known to be positive for Lyme disease and a blood sample known tobe positive for STARI; and the classification model has an accuracy ofat least 80%, at least 85%, at least 90%, or at least 95% for detectinga sample from a subject with Lyme disease and an accuracy of at least80% or at least 85% for detecting a sample from a subject with STARI.Alternatively, abundance values are provided for each molecular featurein Table A, Table B, or Table D; the suitable controls include a bloodsample known to be positive for Lyme disease, a blood sample known to bepositive for STARI, and a blood sample known to be negative for bothLyme disease and STARI; and the classification model has an accuracy ofat least 80%, at least 85%, or at least 90%, even more preferably atleast 95% for detecting a sample from a subject with Lyme disease and anaccuracy of at least 80% or at least 85% for detecting a sample from asubject with STARI. In still another alternative, abundance values areprovided for each molecular feature in Table C or Table D; the suitablecontrols include a blood sample known to be positive for Lyme disease, ablood sample known to be positive for STARI, and a blood sample known tobe negative for both Lyme disease and STARI; and the classificationmodel has an accuracy of at least 80%, preferably at least 85% fordetecting a sample from a subject with Lyme disease; an accuracy of atleast 80%, at least 85%, or at least 90% for detecting a sample from asubject with STARI; and an accuracy of at least 80%, at least 85%, atleast 90%, or at least 95% for detecting a sample from a healthysubject.

V. Methods for Treating a Subject as Having Lyme Disease or STARI

Another aspect of the disclosure is a method for treating a subjectbased on the subject's classification as having Lyme disease or STARI asdescribed in Section IV. Treatment may be with a non-pharmacologicaltreatment, a pharmacological treatment, or an additional diagnostictest.

In one example, the method comprises (a) obtaining a disease score froma test; (b) diagnosing the subject with Lyme disease based on thedisease score; and (c) administering a treatment to the subject withLyme disease, wherein the test comprises measuring the amount of eachmolecular feature in Table A, Table B, Table C, or Table D; providingabundance values for each molecular feature measured; and inputting theabundance values into a classification model trained with samplesderived from suitable controls, wherein the classification modelproduces a disease score and the disease score distinguishes subjectswith Lyme disease from subjects with STARI, and optionally from healthysubjects. In some examples, the test is a method of Section IV. Infurther examples, the test comprises (i) deproteinizing a blood samplefrom a subject to produce a metabolite extract; (ii) performing liquidchromatography coupled to mass spectrometry on a sample of themetabolite extract; (iii) providing abundance values for each molecularfeature in Table A, Table B, Table C or Table D; and (iv) inputting theabundance values from step (iii) into a classification model trainedwith samples of metabolite extracts derived from suitable controls,wherein the classification model produces a disease score and thedisease score distinguishes subjects with Lyme disease. Suitablecontrols are described above. In one example, abundance values areprovided for each molecular feature in Table A, Table B, or Table D; thesuitable controls comprise a blood sample known to be positive for Lymedisease and a blood sample known to be positive for STARI; and theclassification model has an accuracy of at least 80%, at least 85%, atleast 90%, or at least 95% for detecting a sample from a subject withLyme disease and an accuracy of at least 80% or at least 85% fordetecting a sample from a subject with STARI. Alternatively, abundancevalues are provided for each molecular feature in Table A, Table B, orTable D; the suitable controls include a blood sample known to bepositive for Lyme disease, a blood sample known to be positive forSTARI, and a blood sample known to be negative for both Lyme disease andSTARI; and the classification model has an accuracy of at least 80%, atleast 85%, or at least 90%, even more preferably at least 95% fordetecting a sample from a subject with Lyme disease and an accuracy ofat least 80% or at least 85% for detecting a sample from a subject withSTARI. In still another alternative, abundance values are provided foreach molecular feature in Table C or Table D; the suitable controlsinclude a blood sample known to be positive for Lyme disease, a bloodsample known to be positive for STARI, and a blood sample known to benegative for both Lyme disease and STARI; and the classification modelhas an accuracy of at least 80%, preferably at least 85% for detecting asample from a subject with Lyme disease; an accuracy of at least 80%, atleast 85%, or at least 90% for detecting a sample from a subject withSTARI; and an accuracy of at least 80%, at least 85%, at least 90%, orat least 95% for detecting a sample from a healthy subject.

Treatment may comprise one or more standard treatments for Lyme disease.Non-limiting examples of standard pharmacological treatments for Lymedisease include an antibiotic, an antibacterial agent, a vaccine, animmune modulator, an anti-inflammatory agent, or a combination thereof.Suitable antibiotics include, but are not limited to, amoxicillin,doxycycline, cefuroxime axetil, amoxicillin-clavulanic acid, macrolides,ceftriaxone, cefotaxmine, and penicillin G. Antibiotics may beadministered orally or parenterally. Alternatively, treatment maycomprise one or more experimental pharmacological treatment (e.g.,treatment in a clinical trial). In each of the above examples, treatmentmay be for the acute or disseminated stage of the disease, or may be aprophylactic treatment. For example, following successful resolution ofa primary Borrelia infection, the subject may be treated with a vaccineto prevent future infections. In still other examples, treatment maycomprise further diagnostic testing. For example, if a subject has earlyLyme disease but was negative for Lyme disease by current diagnostictesting (e.g., first-tier testing performed using the C6 EIA and secondtier testing using IgM and/or IgG immunoblots following a positive orequivocal first-tier assay), additional testing may be ordered after anamount of time has elapsed (e.g., 3, 5, 7, 10, 14 days or more) toconfirm the initial diagnosis.

In another example, the method comprises (a) obtaining a disease scorefrom a test; (b) diagnosing the subject with STARI based on the diseasescore; and (c) administering a treatment to the subject with STARI,wherein the test comprises measuring the amount of each molecularfeature in Table A, Table B, Table C, or Table D; providing abundancevalues for each molecular feature measured; and inputting the abundancevalues into a classification model trained with samples derived fromsuitable controls, wherein the classification model produces a diseasescore and the disease score distinguishes subjects with STARI fromsubjects with Lyme disease, including early Lyme disease, and optionallyfrom healthy subjects. In some examples, the test is a method of SectionIV. In further examples, the test comprises (i) deproteinizing a bloodsample from a subject to produce a metabolite extract; (ii) performingliquid chromatography coupled to mass spectrometry on a sample of themetabolite extract; (iii) providing abundance values for each molecularfeature in Table A, Table B, Table C or Table D; and (iv) inputting theabundance values from step (iii) into a classification model trainedwith samples of metabolite extracts derived from suitable controls,wherein the classification model produces a disease score and thedisease score distinguishes subjects with STARI. Suitable controls aredescribed above. In one example, abundance values are provided for eachmolecular feature in Table A, Table B, or Table D; the suitable controlscomprise a blood sample known to be positive for Lyme disease and ablood sample known to be positive for STARI; and the classificationmodel has an accuracy of at least 80%, at least 85%, at least 90%, or atleast 95% for detecting a sample from a subject with Lyme disease and anaccuracy of at least 80% or at least 85% for detecting a sample from asubject with STARI. Alternatively, abundance values are provided foreach molecular feature in Table A, Table B, or Table D; the suitablecontrols include a blood sample known to be positive for Lyme disease, ablood sample known to be positive for STARI, and a blood sample known tobe negative for both Lyme disease and STARI; and the classificationmodel has an accuracy of at least 80%, at least 85%, or at least 90%,even more preferably at least 95% for detecting a sample from a subjectwith Lyme disease and an accuracy of at least 80% or at least 85% fordetecting a sample from a subject with STARI. In still anotheralternative, abundance values are provided for each molecular feature inTable C or Table D; the suitable controls include a blood sample knownto be positive for Lyme disease, a blood sample known to be positive forSTARI, and a blood sample known to be negative for both Lyme disease andSTARI; and the classification model has an accuracy of at least 80%,preferably at least 85% for detecting a sample from a subject with Lymedisease; an accuracy of at least 80%, at least 85%, or at least 90% fordetecting a sample from a subject with STARI; and an accuracy of atleast 80%, at least 85%, at least 90%, or at least 95% for detecting asample from a healthy subject.

Treatment may comprise one or more standard treatments for STARI. Thereare no therapeutic agents specifically approved for STARI, in partbecause the causative agent is not known. Nonetheless, non-limitingexamples of standard pharmacological treatments for STARI include anantibiotic, an antibacterial agent, a vaccine, an immune modulator, ananti-inflammatory agent, or a combination thereof. Suitable antibioticsinclude, but are not limited to, amoxicillin, doxycycline, cefuroximeaxetil, amoxicillin-clavulanic acid, macrolides, ceftriaxone,cefotaxmine, and penicillin G. Antibiotics may be administered orally orparenterally. Alternatively, treatment may comprise one or moreexperimental pharmacological treatment (e.g., treatment in a clinicaltrial). In each of the above examples, treatment may be for acutedisease, or may be a prophylactic treatment. For example, followingsuccessful treatment of STARI (as defined the by the current clinicalstandard of the time), the subject may be treated with a vaccine toprevent future infections. In still other another example, treatment maycomprise further diagnostic testing. For example, if a subject isdiagnosed with STARI, additional testing may be ordered after an amountof time has elapsed (e.g., 3, 5, 7, 10, 14 days or more) to confirm theinitial diagnosis. In yet another example, treatment may consist ofsupportive care only, e.g., non-pharmacological treatments orover-the-counter pharmaceutical agents to alleviate symptoms, such asfever, aches, etc.

In certain examples, obtaining a result from a test of Section IVcomprises analyzing a blood sample obtained from the subject asdescribed in Section III and/or classifying the subject as described inSection IV. In certain examples, obtaining a result from a test ofSection IV comprises requesting (e.g., placing a medical order orprescription) from a third party a test that analyzes a blood sampleobtained from the subject as described in Section III and classifies thesubject as described in Section IV, or requesting from a third party atest that analyzes a blood sample obtained from the subject as describedin Section III, and then performing the classification as described inSection IV.

In each of the above examples, the method may further comprise obtaininga second result (for a sample obtained from the subject after treatmenthas begun) from the same test of Section IV as before treatment andadjusting treatment based on the test result.

Accordingly, yet another aspect of the disclosure is a method formonitoring the effectiveness of a therapeutic agent intended to treat asubject with Lyme disease or STARI. The method comprises obtaining aresult from a test of Section IV, administering a therapeutic agent tothe subject, obtaining a result from the same test of Section IV asbefore treatment, wherein the treatment is effective if the diseasescore classifies the subject as more healthy than before. A first sampleobtained before treatment began may be used as a baseline.Alternatively, the first sample may be obtained after treatment hasbegun. Samples may be collected from a subject over time, including 0.5,1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more days apart. For example,blood samples may be collected 0.5, 1, 2, 3, or 4 days apart.Alternatively, blood samples may be collected 4, 5, 6, or 7 days apart.Further, blood samples may be collected 7, 8, 9, or 10 days apart. Stillfurther, blood samples may be collected 10, 11, 12 or more days apart.

The following examples are included to demonstrate preferred examples ofthe invention. It should be appreciated by those of skill in the artthat the techniques disclosed in the examples that follow representtechniques discovered by the inventors to function well in the practiceof the invention. Those of skill in the art should, however, in light ofthe present disclosure, appreciate that changes may be made in thespecific examples that are disclosed and still obtain a like or similarresult without departing from the spirit and scope of the invention.Therefore, all matter set forth or shown in the examples andaccompanying drawings is to be interpreted as illustrative and not in alimiting sense.

EXAMPLES

The following examples illustrate various iterations of the invention.

Example 1

Lyme disease is a multisystem bacterial infection that in the UnitedStates is primarily caused by infection with Borrelia burgdorferi sensustricto. Over 300,000 cases of Lyme disease are estimated to occurannually in the United States, with over 3.4 million laboratorydiagnostic tests performed each year (1, 2). Symptoms associated withthis infection include fever, chills, headache, fatigue, muscle andjoint aches, and swollen lymph nodes; however, the most prominentclinical manifestation in the early stage is the presence of one or moreerythema migrans (EM) skin lesions (3). This annular, expandingerythematous skin lesion occurs at the site of the tick bite in 70 to80% of infected individuals and is typically 5 cm or more in diameter(4, 5). Although an EM lesion is a hallmark for Lyme disease, othertypes of skin lesions can be confused with EM (3, 5, 6). These includerashes caused by tick-bite hypersensitivity reactions, certain cutaneousfungal infections, bacterial cellulitis and the rash of southerntick-associated rash illness (STARI) (7, 8).

STARI is associated with a bite from the lone star tick (Amblyommaamericanum) and, in addition to the development of an EM-like skinlesion, individuals with STARI can present with mild systemic symptoms(including muscle and joint pains, fatigue, fever, chills, and headache)that are similar to those occurring in patients with Lyme disease (7, 9,10). These characteristics of STARI have led some to postulate that theetiology of this illness is a Borrelia species, including B. burgdorferi(10, 11) or B. lonestari (12-15); however, multiple studies have refutedthat STARI is caused by B. burgdorferi (7, 16-19) and additional casesassociating B. lonestari with STARI have not emerged (20, 21).Additionally, STARI patients have been screened serologically forreactivity to rickettsial agents, but no evidence was obtained todemonstrate that rickettsia causes this illness (10, 22). Thus, atpresent no infectious etiology is known for STARI.

STARI cases occur over the geographic region where the lone star tick ispresent. This includes a region that currently expands from centralTexas and Oklahoma upward into the Midwestern states and eastward,including the southern states and along the Atlantic coast into Maine(23). Unlike STARI, Lyme disease is transmitted to humans through thebite of the blacklegged tick (Ixodes scapularis) that is present in thenortheastern, mid-Atlantic, and north-central United States, and thewestern blacklegged tick (I. pacificus), which is present on the PacificCoast (24). The geographic distribution of human Lyme disease and thevectors for this disease is expanding (24-26), and there is a similarexpansion of areas inhabited by the lone star tick (23). Importantly, astrict geographic segregation of Lyme disease and STARI does not exist,as there are regions where STARI and Lyme disease are co-prevalent (25).Thus, there is a growing need for diagnostic methods to differentiatebetween Lyme disease and STARI, and that facilitate proper treatment,patient management and disease surveillance.

Clinically, the skin lesions of STARI and early Lyme disease areindistinguishable, and no laboratory tool or method exists for thediagnosis of STARI or differentiation of STARI from Lyme disease. Theonly biomarkers evaluated for differential diagnosis of early Lymedisease and STARI have been serum antibodies to B. burgdorferi (10, 16).However, these tests have poor sensitivity for early stages of Lymedisease, and thus a lack of B. burgdorferi antibodies cannot be used asa reliable differential marker for STARI.

The experiments herein describe the development of a metabolomics-drivenapproach to identify biomarkers that discriminate early Lyme diseasefrom STARI, and provide evidence that these two diseases arebiochemically distinct. A retrospective cohort of well-characterizedsera from patients with early Lyme disease and STARI was evaluated toidentify a differentiating metabolic biosignature. Using statisticalmodeling, this metabolic biosignature accurately classified test samplesthat included healthy controls. Additionally, the metabolic biosignaturerevealed that N-acyl ethanolamine (NAE) and primary fatty acid amide(PFAM) metabolism differed significantly between these two diseases.

Clinical Samples:

A total of 220 well-characterized retrospective serum samples from threedifferent repositories were used to develop and test a metabolicbiosignature that accurately classifies early Lyme disease and STARI(Table 2). All samples from Lyme disease patients were culture confirmedand/or PCR positive for B. burgdorferi. The median age for early Lymedisease patients was 45 years and 74% were males. STARI patients had anoverall median age of 45 years and 55% were males.

To establish a Lyme disease diagnostic baseline, the recommendedtwo-tiered serology testing for Lyme disease was performed on allsamples. First-tier testing was performed using the C6 EIA and waspositive for 66% of Lyme disease samples. When STARI and healthycontrols were tested by the C6 EIA, two STARI samples (2%) and fivehealthy controls (9%) tested positive or equivocal. Two-tiered testingusing IgM and IgG immunoblots as the second-tier test following apositive or equivocal first-tier assay resulted in a sensitivity of 44%for early Lyme disease samples (duration of illness was not consideredfor IgM immunoblot testing). The sensitivity of two-tiered testing forearly Lyme disease samples included in the Discovery/Training-Sets andthe Test-Sets was 38% and 53%, respectively. All STARI and healthycontrol samples were negative by two-tiered testing (Table 2).

Development of a Metabolic Biosignature for Early Lyme Disease and STARIDifferentiation:

Metabolic profiling by liquid chromatography-mass spectrometry (LC-MS)of a retrospective cohort of well-characterized sera from patients withearly Lyme disease (n=40) and STARI (n=36) (Table 2 and FIG. 1)comprising the Discovery-Set (i.e. Test-Set samples that were not usedin molecular feature selection) resulted in a biosignature of 792molecular features (MFs) that differed significantly (adjusted-p<0.05)with a ≥2 fold change in relative abundance between early Lyme diseaseand STARI. Down-selection of MFs based on their robustness in replicateanalyses of the same sera produced a refined biosignature of 261 MFs(FIG. 1 and Table 3). Of these 261 MFs, 60 and 201 displayed anincreased and decreased abundance, respectively, in early Lyme diseaseas compared to STARI. The large number of MFs that differedsignificantly between early Lyme disease and STARI patients indicatedthat these two patient groups had distinguishing biochemical profiles.These variances were applied to define alterations of specific metabolicpathways (FIG. 1) and used to develop diagnostic classification models(FIG. 1).

In Silico Analysis of Metabolic Pathways:

Presumptive chemical identification was applied to the 261 MFs. Thisyielded predicted chemical formulae for 149 MFs, and 122 MFs wereassigned a putative chemical structure based on interrogation of eachMF's monoisotopic mass (+ or −15 ppm) against the Metlin database andthe Human Metabolome Database (HMDB) (Table 3). An in silicointerrogation of potentially altered metabolic pathways was performedusing the presumptive identifications for the 122 MFs and MetaboAnalyst(28). Four differentiating pathways were predicted to have the greatestimpact, with the most significant being glycerophospholipid metabolismand sphingolipid metabolism (FIG. 2 and Table 4). Specifically, theMetaboAnalyst analysis indicated that differences in phosphatidic acid,phosphatidylethanolamine, phosphatidylcholine andlysophosphotidylcholine were the major contributors to alteredglycerophospholipid metabolism between STARI and early Lyme disease(Table 4). Altered sphingolipid metabolism between these two groups wasattributable to changes in the relative abundances of sphingosine,dehydrosphinganine and sulfatide (Table 4). Manual interrogation of thepredicted structural identifications revealed that 26 and 7 of the 122MFs assigned a putative structural identification were associated withglycerophospholipid and sphingolipid metabolism, respectively (Table 3).

Elucidation of Altered NAE Metabolism:

The prediction of altered metabolic pathways was based on thepresumptive structural identification of the early Lyme disease versusSTARI differentiating MFs. Thus, to further define the metabolicdifferences between these two patient groups, structural confirmation ofselected MFs was undertaken. Two MFs that displayed relatively largeabundance differences (m/z 300.2892, RT 19.66; and m/z 328.3204, RT20.72) were putatively identified as sphingosine-C18 or3-ketosphinganine, and sphingosine-C20 or N,N-dimethyl sphingosine,respectively. However, both of these MFs had alternative predictedstructures of palmitoyl ethanolamide and stearoyl ethanolamide,respectively. The interrogation of authentic standards against these twoserum MFs revealed RTs and MS/MS spectra that identified the m/z300.2892 and m/z 328.3204 products as palmitoyl ethanolamide (FIGS. 3Aand 3B) and stearoyl ethanolamide (FIG. 7), respectively. These twoproducts, as well as other NAEs, are derived fromphosphatidylethanolamine and phosphatidylcholine, and represent a classof structures termed endocannabinoids and endocannabinoid-like (29)(FIG. 3C). Further analysis of the 122 MFs identified five additionalMFs with a predicted structure that mapped to the NAE pathway.Specifically, MF m/z 286.2737, RT 19.08 was putatively identified as asphingosine-C17 or pentadecanoyl ethanolamide, and was confirmed to bethe latter (FIG. 8). MF m/z 356.3517, RT 21.67 was putatively identifiedand confirmed to be eicosanoyl ethanolamide (FIG. 9), and MF m/z454.2923, RT 18.08 was confirmed to be glycerophospho-N-palmitoylethanolamine (FIG. 10), which is an intermediate in the formation ofpalmitoyl ethanolamide. A second group of lipids, the PFAMs that act assignaling molecules and that are potentially associated with themetabolism of NAEs were also identified as having significant relativeabundance differences between the early Lyme disease and STARI patientsamples. Specifically, MFs m/z 256.2632, RT 20.08; m/z 284.2943, RT21.15; and m/z 338.3430, RT 22.14 were confirmed to be palmitamide (FIG.3D and FIG. 3E), stearamide (FIG. 11) and erucamide (FIG. 12),respectively.

The large number of differentiating MFs associated with NAE metabolismsuggested that this is a major biological difference between STARI andearly Lyme disease (FIG. 3C and Table 3). Four additional MFs of the 261MF biosignature, and that fit known host biochemical pathways, were alsostructurally confirmed. These included L-phenylalanine (FIG. 13),nonanedioic acid (FIG. 14), glycocholic acid (FIG. 15) and3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF) (FIG. 16).Additionally, two MFs that provided strong matches to MS/MS spectra inthe Metlin databases were putatively identified as arachidonoyllysophosphatidic acid [Lyso PA (20:4)] (FIG. 17) and 3-ketosphingosine(FIG. 18).

The 261 MF Biosignature List Revealed Metabolic Dissimilarity BetweenLyme Disease and STARI:

To test whether early Lyme disease and STARI represent distinctmetabolic states that would be reflected in the comparison of MFabundances in these two disease states to those of healthy controls, theabundance fold-change for each structurally confirmed MF in early Lymedisease and STARI sera as compared to healthy controls was determined.This revealed that the majority of these MFs maintained fold changedifferences with respect to healthy controls that allowed forsegregation of early Lyme disease and STARI patient samples (FIG. 4A).For three MFs (3-ketosphingosine, CMPF, and Lyso PA 20:4), the levels inearly Lyme disease were increased as compared to the healthy controlswhile the levels in STARI were decreased. Additionally, all of the NAEsand PFAMs had abundances in early Lyme disease patients that were closerto those of healthy controls, whereas the abundances in STARI weregreatly increased. This analysis was expanded to all 261 MFs of theearly Lyme disease-STARI biosignature (FIG. 4B). The percent of MFs withincreased and decreased abundances relative to healthy controls weresimilar across the abundance fold changes for both early Lyme diseaseand STARI. However, when the MFs with increased or decreased abundanceswere compared between early Lyme disease and STARI for each range ofabundance fold change, the concordance was low (0 to 30%) (FIG. 4C).This indicated that the metabolic changes in early Lyme disease andSTARI as compared to healthy controls differed.

Diagnostic Classification of Early Lyme Disease Vs STARI:

Classification models were used to determine whether the 261 MFbiosignature could be applied to discriminate early Lyme disease fromSTARI (Table 1 and FIG. 1). Specifically, two classification models,least absolute shrinkage and selection operator (LASSO) and randomforest (RF) were trained with the 261 MF biosignature using abundancedata from the Training-Set samples only (FIG. 1). Test-Set samples werenot used for molecular feature selection or to train the classificationmodels. The LASSO model selected 38 MFs, and RF by default does notperform feature selection and thus used all 261 MFs for classificationof the STARI and early Lyme disease patient populations (Table 3 andTable 5). When Test-Set samples (FIG. 1) (i.e. those not included in theDiscovery/Training-Set) were tested in duplicate, early Lyme diseasesamples were classified by RF and LASSO with an accuracy of 97% and 98%,respectively. The STARI samples had a classification accuracy of 89%with both models (Table 1 and Table 6). A depiction of the LASSO scoresfor the Test-Set data showed segregation of the early Lyme disease andSTARI patient samples, and demonstrated the discriminating power of the38 MFs selected by the LASSO model (FIG. 5A). A receiver operatingcharacteristic (ROC) curve was plotted to demonstrate the performance ofthe LASSO model for differentiating early Lyme disease from STARIpatients. The area under the curve (AUC) was calculated to be 0.986(FIG. 5B). The 38 MFs of the LASSO model encompassed four of the 14structurally confirmed metabolites: CMPF, L-phenylalanine, palmitoylethanolamide, and arachidonoyl lysophosphatidic acid (Table 3).

Diagnostic Classification of Early Lyme Disease Vs STARI Vs HealthyControls:

Separate three-way classification models using LASSO and RF weredeveloped by including LC-MS data collected for healthy controls in theTraining-Set samples (FIG. 1). For model training LASSO selected 82 MFs(Table 3). The regression coefficients for the 82 MFs selected by LASSOare provided in Table 7. Evaluation of the RF and LASSO three-wayclassification models with Test-Set samples (those not used in theDiscovery/Training-Sets) revealed classification accuracies of 85% and92% for early Lyme disease and STARI, respectively. Surprisingly,healthy controls were classified with accuracies of 95% and 93% with theRF and LASSO models, respectively (Table 1 and Table 8). Plotting ofLASSO scores calculated for Test-Set data revealed three groupings thatcorresponded with early Lyme disease, STARI and healthy controls (FIG.5C). Of the early Lyme disease samples that were misclassified with theRF model (n=9), all were predicted to be healthy controls; and thosemisclassified by the LASSO model (n=9), three were classified as STARIand six as healthy controls. Of the STARI samples that weremisclassified by the RF and LASSO models (n=3 for both models), allsamples were misclassified as early Lyme disease. When healthy controlswere misclassified using the RF model (n=2) and LASSO model (n=3), allwere misclassified as early Lyme disease.

Of the 38 MFs selected by LASSO for the two-way classification model, 33were included in the 82 MFs of the LASSO three-way classification model(Table 3). The 82 MFs of the LASSO three-way classification includedseven of the 14 structurally confirmed metabolites: 3-ketosphingosine,glycocholic acid and pentadecanoyl ethanolamide, as well as the fourincluded in the LASSO two-way classification model (Table 3).

Biosignature was not Influenced by Geographic Variability:

Since retrospective samples collected by multiple laboratories were usedin these studies, analyses were performed to assess whether a geographicbias was introduced. To address this, three healthy control groups andthree STARI groups (all early Lyme disease samples came from onegeographic region) were evaluated by linear discriminant analysis usingthe 82 MFs of the LASSO three-way classification model (FIG. 6). Forhealthy controls, those samples used in the modeling (collected in NewYork and Colorado) were evaluated. Additionally, healthy controls fromFlorida, a region with low prevalence for Lyme disease and reported tohave STARI cases, were included to evaluate whether samples collected inthe southern United States would differ from those collected in New Yorkor Colorado. For STARI, three patient samples groups collected inMissouri, NC and other states (included VA, GA, KY, TN, AL, IA and NE)were compared. The linear discriminant analysis demonstrated thatalthough slight variation exists between the three healthy controlgroups (NY, CO and FL), there is greater variability between all healthycontrols and all STARI samples than within healthy controls or STARIsamples based on geographic location of collection (FIG. 6).

Discussion:

The inability to detect B. burgdorferi by PCR or culture and the lack ofa serological response to B. burgdorferi antigens in STARI patients iswidely accepted as evidence that the etiologies of STARI and Lymedisease differ (7, 16). This is further supported by the different tickspecies associated with these two diseases (8, 25). Nevertheless, thestrong overlap in clinical symptoms, including the development of anEM-like skin lesion, creates confusion and controversy for the clinicaldifferentiation of STARI and Lyme disease (30). The data reported heredemonstrated marked differences between the metabolic profiles of earlyLyme disease and STARI patients, and thus provide compelling positivedata to support the concept that these two illnesses are distinctentities. Interestingly, metabolic pathway analyses and the structuralidentification of several MFs with significant abundance differencesbetween early Lyme disease and STARI identified multiple NAEs. Theseendogenous lipid mediators are derived from phosphatidylcholine andphosphatidylethanolamine via the endocannabinoid system (FIG. 3C) (29).Arachidonoylethanolamide (AEA) is the most widely studiedendocannabinoid, as it is an endogenous agonist of the cannabinoidreceptors; however, it is a minor component of animal tissues. Incontrast, congeners of AEA, such as the NAEs identified in the earlyLyme disease-STARI biosignature, are significant products of animaltissues, including the skin (29, 31). The serum levels of NAEspossessing long-chain saturated fatty acids were significantly increasedin the serum of STARI patients. These NAEs are produced in response toinflammation, and act in an anti-inflammatory manner as agonists ofPPAR-α or by enhancing AEA activity (32, 33). The NAEs are generallydegraded via fatty acid amide hydrolase; however, it was recentlydemonstrated that NAEs can be converted to N-acylglycine structures viaan alcohol dehydrogenase, and further degraded to PFAMs (34).Interestingly, the data generated from these studies not onlydemonstrated a STARI-associated increase in NAEs with saturated fattyacids, but also an increase in the corresponding PFAMs. Although themechanism for the increased abundance of NAEs and PFAMs in STARIpatients is unknown, decrease in fatty acid amide hydrolase activitywhich releases free fatty acids from both NAEs and PFAMs would result inthe observed increase in abundance of these metabolites (35). Theanti-inflammatory activity of the NAEs also raises the possibility thatthese metabolites are partially responsible for the milder symptomsassociated with STARI (9). As the enzymes involved with the genesis anddegradation of NAEs and PFAMs are known (29, 36), studies can beconstructed to further elucidate the mechanism(s) by which NAEs andPFAMs accumulate in the sera of STARI patients.

This current work expands demonstrates the ability to distinguish earlyLyme disease from an illness with nearly identical symptoms or whatwould be considered a Lyme disease-like illness (37). The existingdiagnostic algorithm for Lyme disease is a two-tiered serologic approachthat utilizes an EIA or IFA as a first-tier test followed by IgM and IgGimmunoblotting as the second-tier test (38). For early Lyme disease, thesensitivity of this diagnostic is 29-40% and the specificity is 95-100%(39). The current antibody-based approaches do not distinguish betweenactive and previous infections, an important limitation. In the currentstudy all of the STARI samples were negative by two-tiered testing, andonly 2% were positive by the first-tier EIA. Early Lyme disease sampleswere 44% positive (38% positivity for the early Lyme disease samplesused in the Discovery and Training Sets and 53% positivity for earlyLyme disease samples used in the Test Sets) by two-tiered testing. Incontrast, when classification modeling was applied to the 261 MFs of theearly Lyme disease-STARI biosignature, diagnostic accuracy for earlyLyme disease was dramatically increased (85 to 98% accuracy depending onthe model) as compared to serology. Classification by RF or LASSO wasoverall highly accurate for early Lyme disease and STARI, in particularwhen using the two-way classification models. Interestingly, whenhealthy controls were introduced and used to develop a three-wayclassification model there was a slight increase in the accuracy forSTARI and decrease in the accuracy for early Lyme disease, but healthycontrols were classified with a 93-95% accuracy. This was surprising ashealthy controls were not used to create the initial 261 MFbiosignature, and furthers supported that STARI and early Lyme diseaseare metabolically distinct from healthy controls, but in different ways.

To date the development of a diagnostic tool for STARI or fordifferentiation of early Lyme disease and STARI has received littleattention. As the geographic distribution of Lyme disease continues toexpand (25, 26), so will the geographic range where there is overlap ofLyme disease and STARI. Thus, a diagnostic tool that accuratelydifferentiates these two diseases could have a major impact on patientmanagement. Lyme disease is treated with antibiotics, and although thereis no defined infectious etiology for STARI, this illness is alsocommonly treated in a similar manner (7, 20, 40). Establishment of arobust diagnostic tool would not only facilitate antibiotic stewardship,it would also allow for proper studies to assess the true impact oftherapies for STARI. Lyme disease is also a reportable disease and inorder to maintain accurate disease surveillance in low incidence areas,it is essential that diseases such as STARI be excluded (30).Additionally, vaccines are currently being developed for Lyme disease(41-44) and as these are tested, it will be important to identify STARIpatients in order to properly assess vaccine efficacy.

To apply the discoveries of this work towards the development of anassay that can be used for the clinical differentiation of early Lymedisease and STARI, it should first be determined whether an emphasisshould be placed on the diagnosis of Lyme disease or STARI. As there isno defined etiology of STARI, and Lyme disease is not necessarilyself-limiting without antibiotics and can have subsequent complicationsif untreated, we envision that the final assay would focus on beinghighly sensitive for early Lyme disease and be primarily applied inregions where Lyme disease and STARI overlap. Although existinglaboratory tests for Lyme disease emphasize specificity, this strategyneeds to be reconsidered for a differential diagnostic test of STARI andearly Lyme disease, since any illness presenting with an EM in a regionwith a known incidence of Lyme disease would likely be treated withantibiotics (7, 20, 40). As with all diagnostic tests, use of ametabolic biosignature for differentiation of early Lyme disease andSTARI would need to be performed in conjunction with clinical evaluationof the patient, and consideration of their medical history andepidemiologic risk for these two diseases.

The approach outlined in this study applies semi-quantitative massspectrometry and the use of biochemical signatures for theclassification of patients. Clinical application of such an approachwould likely occur in a specialized clinical diagnostic laboratory.However, it should be noted that the second-tier immunoblot assays forthe serological diagnosis of Lyme disease are already performed inspecialized laboratories (1, 45, 46). Mass spectrometry assays arecurrently used in clinical laboratories for the analyses of smallmolecule metabolites. The majority of these tests are under ClinicalLaboratory Improvement Amendments (CLIA) guidelines, but an FDA clearedmass spectrometry-based test for inborn metabolic errors is in use (47).The most accurate quantification of metabolites by mass spectrometry isachieved by Multiple Reaction Monitoring (MRM) assays (48). Such assaysare developed with the knowledge of a MF's chemical structure. To thisend, the chemical structure of 14 MFs have been identified. The chemicalstructure of the remainder of the MFs can be identified by the methodsdescribed herein. It should be noted that the NAEs and PFAMs that wererevealed via our pathway analyses are amenable to MRM assays (49). Thesemetabolites are now being investigated for their ability to accuratelyclassify STARI and early Lyme disease.

The data reported here were generated from the analysis ofretrospectively collected serum samples from various repositories thathave been archived for different lengths of time. To reduce the impactof the potential variability associated with these samples, stringentcriteria were applied to the data analysis. In addition to therequirement of a significant fold change, those MFs selected for thefinal early Lyme disease-STARI biosignature were required to be presentin at least 80% of samples within a sample group and maintain the medianfold-change difference in at least 50% of samples within a group. Whilethe STARI and healthy control sera were collected by multiplelaboratories and from multiple geographic locations, the early Lymedisease sera were obtained from a single laboratory. This is a potentiallimitation of the study. However, linear discriminate analysis wasapplied to assess the variability within the healthy control and STARIsamples collected by different laboratories. This analysis demonstratedlittle to no variability among the STARI or healthy control samplesindicating that the criteria used for MF selection effectively reducednon-biological variability. As noted, data were collected bynon-absolute semi-quantitative mass spectrometry. Nevertheless, this isa common practice applied in the development of differentiatingbiosignatures for infectious diseases (27, 50-53), and the workflowensured that the most robust MFs were selected and used forclassification modeling.

Without knowledge of a known etiologic agent, it is recognized thatSTARI simply encompasses a clinical syndrome. The STARI samples used inthis current work included those collected in studies used to definethis illness (9), as well as samples collected outside those originalstudies. Additional samples collected prospectively will be useful toassess the applicability of our current metabolic biosignature in a realworld scenario. Future sample collection will also target patientpopulations with non-Lyme EM-like lesions, including tick-bitehypersensitivity reactions, certain cutaneous fungal infections andbacterial cellulitis. Additionally, other factors such as confectionswith other vector-borne pathogens will need to be addressed withprospective studies. In the Southeastern United States, there isevidence for enzootic transmission of B. burgdorferi; however, it isdebatable whether Lyme disease occurs in this region (11, 30, 54, 55).The current study was not designed to provide evidence for or againstthe presence of Lyme disease in the southern United States.Nevertheless, metabolic profiling offers a novel approach that isorthogonal to the methods currently employed to address this issue.

Example 2

STARI is an illness that has received little attention over the years,but is a confounding factor in diagnosing early Lyme disease in areaswhere both illnesses overlap and contributes to the debate surroundingthe presence of Lyme disease in the southern United States. Nodiagnostic tool exists for STARI or for differentiating early Lymedisease from STARI. Based on documented differences between early Lymedisease and STARI (9, 16, 56), we metabolically profiled serum todevelop a biochemical biosignature that when applied could accuratelyclassify early Lyme disease and STARI patients (See Example 1). Thisexample describes the design of the study described in Example 1.

An unbiased-metabolomics study was designed to directly compare themetabolic host responses between these two illnesses, and subsequentlyevaluate how this metabolic biosignature distinguishes these twoillnesses. The use of unbiased metabolomics for biosignature discoverydoes not lend itself to power calculations to determine sample size.Thus, sample sizes were selected based on our previous studies (27, 50,51). To obtain a sufficient number of well-characterized STARI sera,retrospectively collected samples from two separate studies were used.Specifically, the first set of STARI serum samples (n=33) was obtainedfrom the CDC repository. These samples were collected through aprospective study performed between 2007 and 2009 (57). Patients wereenrolled through CDC outreach efforts (n=17) or by contract with theUniversity of North Carolina at Chapel Hill (n=16). The states wherepatients were recruited included NC, 18; VA, 4; TN, 3; KY, 2; GA, 2; IA,2; AL, 1; and NE, 1. All samples were collected pre-treatment with theexception of one patient who was treated with doxycycline 1-2 daysbefore the serum sample was obtained. The second set of STARI samples(n=22) was obtained from the New York Medical College serum repository(20). These samples were collected between 2001 and 2004 from patientsliving in Missouri.

Sufficient numbers of well-characterized early Lyme disease serumsamples were acquired from New York, an area of high incidence for Lymedisease and low incidence of STARI (9). Specifically, all early Lymedisease samples (n=70) were culture and/or PCR positive for B.burgdorferi and were collected pre-treatment. To ensure appropriaterepresentation of both non-disseminated and disseminated forms of earlyEM Lyme disease, samples from patients with a single EM that were skinculture and/or PCR positive for B. burgdorferi and blood culturenegative (n=35), and patients with multiple EMs or a single EM that wereblood culture positive (n=35) were used. Early Lyme disease samples werecollected between 1992 and 2007, and 1 to 33 days post-onset ofsymptoms. To understand the relationship of our findings to a healthycontrol population serum samples from healthy donors were also includedin the study. These were procured from repositories at New York MedicalCollege, the CDC and the University of Central Florida. A detaileddescription of inclusion and exclusion criteria for each patient anddonor population is provided in Table 2. All participating institutionsobtained institutional review board (IRB) approval for this study. IRBreview and approval for this study ensured that the retrospectivesamples used had been collected under informed consent.

All samples were analyzed in duplicate and were randomized prior toprocessing for LC-MS analyses. Healthy control sera were used as qualitycontrol samples for each LC-MS experiment. The serum samples andrespective LC-MS data files of each patient group and healthy controlswere randomly separated into a Discovery-Set/Training-Sets 1 and 2, andTest-Sets 1 and 2. Specifically, 40 of the 70 early Lyme disease and 36of the 55 STARI samples were randomly selected as the Discovery-Setsamples. This sample set was used for molecular feature selection. Totrain the classification models, two training-sets were used. The first,Training-Set 1, was identical to the Discovery-Set (i.e. contained thesame early Lyme disease and STARI samples) and the second, Training-Set2, had the same samples as Training-Set 1 with the addition of 38 of the58 healthy control samples. Lastly, Test-Sets 1 and 2 were created.Test-Set 1 was comprised of 30 early Lyme disease and 19 STARI samplesthat were not included in the Discovery/Training samples sets. Test-Set2 had the same samples as those used in Test-Set 1 with the addition of20 healthy control samples that were not included in the Training-Set 2samples. Test-Sets 1 and 2 were exclusively used for blinded testing ofthe classification models.

Randomization into Discovery/Training-Sets or Test-Sets was done in amanner that ensured bias was not introduced based on the repository fromwhich STARI samples were obtained or on whether the early Lyme diseasesamples were from a non-disseminated or disseminated case. Biosignaturedevelopment was performed by screening MFs based on stringent criteriaoutlined in FIG. 1 and detailed in the Biosignature development section(below).

Example 3

This example describes methods used for Lyme disease serologic testingof all serum samples used in the examples above. Standard two-tieredtesting was performed on all samples (38). The C6 B. burgdorferi (Lyme)ELISA (Immunetics, Boston, Mass.) was used as a first-tier test, and anypositive or equivocal samples were reflexed to Marblot IgM and IgGimmunoblots (MarDx Diagnostics, Inc., Carlsbad, Calif.) as thesecond-tier test. Serologic assays were performed according to themanufacturer's instructions, and the data were interpreted according toestablished CDC guidelines (38). Duration of illness, however, was notconsidered for test interpretation.

Example 4

This example describes liquid chromatography-mass spectrometry (LC-MS)methods used in the examples above. Serum samples were randomized priorto extraction of small molecule metabolites and LC-MS analyses. Smallmolecule metabolites were extracted from sera as previously reported(27). An aliquot (10 μl) of the serum metabolite extract was applied toa Poroshell 120, EC-C8, 2.1×100 mm, 2.7 μm LC Column (AgilentTechnologies, Palo Alto, Calif.). The metabolites were eluted with a2-98% nonlinear gradient of acetonitrile in 0.1% formic acid at a flowrate of 250 μl/min with an Agilent 1200 series LC system. The eluent wasintroduced directly into an Agilent 6520 quadrapole time of flight mass(Q-TOF) spectrometer and MS was performed as previously described (27,50). LC-MS and LC-MS/MS data were collected under the followingparameters: gas temperature, 310° C.; drying gas at 10 liters per min;nebulizer at 45 lb per in²; capillary voltage, 4,000 V; fragmentationenergy, 120 V; skimmer, 65 V; and octapole RF setting, 750 V. Thepositive-ion MS data for the mass range of 75 to 1,700 Da were acquiredat a rate of 2 scans per sec. Data were collected in both centroid andprofile modes in 4-GHz high-resolution mode. Positive-ion referencemasses of 121.050873 m/z and 922.009798 m/z were introduced to ensuremass accuracy. To monitor instrument performance, quality controlsamples having a metabolite extract of healthy control serum(BioreclamationIVT, Westbury, N.Y.) was analyzed in duplicate at thebeginning of each analysis day and every 20 samples during the analysisday.

Example 5

This example describes the methods used for biosignature development asdescribed in the examples above. LC-MS data from an initialDiscovery-Set of samples comprised of randomly selected early Lymedisease (n=40) and randomly selected STARI patients (n=36) that wereexclusively used for molecular feature selection and classificationmodel training were processed with the Molecular Feature Extractoralgorithm tool of the Agilent MassHunter Qualitative Analysis softwareversion B.05.00 (Agilent Technologies, Santa Clara, Calif.). The MFswere aligned between data files with a 0.25 min retention time windowand 15 ppm mass tolerance. Comparative analyses of differentiating MFsbetween patient groups were performed using the workflow presented inFIG. 1A. Specifically, the Discovery-Set data was analyzed using MassProfiler Pro (MPP) software version B.12.05 (Agilent Technologies).Using MPP a univariate, unpaired t-test was performed on each metaboliteto test for a difference in mean (standardized) abundance between earlyLyme disease and STARI groups. Multiple testing was accounted for bycomputing false-discovery rate (FDR)-adjusted p-values (Benjamin andHochberg, 1995). To prevent selection of MFs biased by uncontrolledvariables (diet, other undisclosed illnesses, etc.), only MFs present in50% or more of samples in at least one group and that differed betweenthe groups with a significance of adjusted-p<0.05 were selected.Quantitative Analysis software version B.05.01 (Agilent Technologies)was used to extract area abundance values for all differentiallyselected MFs from the MS data files. Duplicate MFs were removed byassessing adduct ions, as well as mass, retention time and abundancesimilarities; this resulted in the Discovery MF List. A duplicate LC-MSanalysis of the Discovery-Set samples was performed and the areaabundance for MFs of the discovery MF List were extracted using theQuantitative Analysis software. These data with those from the firstLC-MS analysis formed the Targeted-Discovery-Set.

Abundance data from the Targeted-Discovery-Set data files werenormalized using a two-step method. First, abundances (area under thepeak for the monoisotopic mass) of each Discovery MF were normalized bythe median intensity of the stable MFs detected in each individualsample (58). Stable MFs were those identified in the original extractionof LC-MS data files with the Agilent MassHunter Qualitative Analysissoftware and present in at least 50% of all sample data files. Secondly,median fold changes of stable MFs between the initial quality controlsample (applied at the beginning of the LC-MS analysis) and each of thesubsequent quality control samples (applied every 20 clinical samplesthroughout the LC-MS analysis) were calculated. The median fold changecalculated for the quality control sample that directly followed eachseries of 20 clinical samples was multiplied against the normalizedDiscovery-MF abundances in the clinical samples of that series. Thissecond normalization step was performed to correct for instrumentvariability. To apply stringency to the development of a final earlyLyme disease-STARI biosignature, MFs were filtered based on consistencyin the duplicate LC-MS data sets by requiring the same directionalabundance change between the patient groups. Specifically, MFs with atleast a ≥2-fold abundance difference and a 1.5-fold abundance differencebetween the medians of the two groups (early Lyme disease and STARI) forLC-MS analysis-1 and LC-MS analysis-2, respectively, were selected.Further criteria applied to ensure that the most robust MFs were beingselected included: removing MFs with >20% missing values in both groups,and selecting only MFs where at least 50% of the samples within apatient group produced a fold change of ≥2 in comparison to the mean ofthe other patient group. This selection process resulted in the MFsincluded in the early Lyme disease-STARI biosignature.

Example 6

This example describes the methods used for prediction and verificationof MF chemical structure. Confirmation of the chemical structures ofselected MFs was performed by LC-MS-MS to provide level-1 or level-2identifications (59). Commercial standards palmitoyl ethanolamide,stearoyl ethanolamide, eicosanoyl ethanolamide,glycerophospho-N-palmitoyl ethanolamine, pentadecanoyl ethanolamide, anderucamide were obtained from Cayman Chemical (Ann Arbor, Mich., USA).Commercial standards piperine and nonanedioic acid were obtained fromSigma Aldrich (Saint Louis, Mo., USA). Commercial standards methyloleate, stearamide, palmitamide, CMPF, and glycocholic acid wereobtained from Santa Cruz Biotechnology, Inc. (Santa Cruz, Calif., USA).The LC conditions used were the same as those used for the LC-MSanalyses of serum metabolites. MS/MS spectra of the targeted MFs andcommercial standards were obtained with an Agilent 6520 Q-TOF massspectrometer. Electrospray ionization was performed in the positive ionmode as described for MS analyses, except the mass spectrometer wasoperated in the 2 GHz extended dynamic range mode. The positive ionMS/MS data (50 to 1,700 Da) were acquired at a rate of 1 scan per sec.Precursor ions were selected by the quadrupole and fragmented viacollision-induced dissociation (CID) with nitrogen at collision energiesof 10, 20, or 40 eV. To provide a level-1 identification, the MS/MSspectra of the targeted metabolites were compared to spectra ofcommercial standards. Additionally, LC retention time comparisonsbetween the targeted MF and the respective standard were made. Aretention time window of ±5 sec was applied as a cutoff foridentification. The MS/MS spectra of selected serum metabolites werecompared to spectra in the Metlin database for a level-2 identification.

Example 7

Metabolic pathway analysis in the examples above was performed byMetaboAnalyst. The experimentally obtained monoisotopic massescorresponding to the MFs of the 261 biosignature list were searchedagainst HMDB using a 15 ppm window. The resulting list of potentialmetabolite structures were applied to the MetaboAnalyst pathway analysistool (28) Settings for pathway analysis included applying Homo sapienspathway library; the Hypergeometric Test for the over-representationanalysis and Relative-betweenness centrality to estimate node importancein the pathway topology.

Example 8

Methods for statistical analyses and classification modeling aredescribed in this example. Methods to filter the list of MFs and tonormalize abundances are described in the section on biosignaturedevelopment. Prior to analysis, the normalized abundances were log 2transformed and each MF was scaled to have a mean of zero and standarddeviation of 1. Statistical analyses were performed using R software(60).

For classification modeling, Training- and Test-Set samples were used aspreviously described (27, 50) and as shown in FIG. 1. Separateclassification analyses were performed for comparison of two groups(early Lyme disease and STARI) and three groups (early Lyme disease,STARI and healthy controls). For each scenario, two classificationapproaches were applied: random forest (RF) using the Random Forestpackage (61), with 16 features randomly selected for each clade and atotal of 500 trees; and LASSO logistic (two-way) and multinomial(three-way) regression analysis using the glmnet package (62), with thetuning parameter chosen for minimum misclassification error over a10-fold cross-validation. The ROC curve and AUC were generated forpredicted responses on the Test-Set samples only using the pROC package(63). For the purpose of visualization, LASSO scores for individualpatient samples were calculated by multiplying the respective regressioncoefficients (Table 5 and Table 7) resulting from LASSO analysis by thetransformed abundance of each MF in the biosignature (38 MFs in the caseof two-way classification and 82 MFs in the case of three-wayclassification) and summing for each sample. The rgl package was used togenerate the 3-dimensional scatterplot of LASSO scores (64).

A linear discriminant analysis was performed with the 82 MFs selected bythe three-way LASSO model using linear discriminant analysis function inR. MF abundance data included in the linear discriminant analysis werefrom healthy controls from Colorado, Florida, and New York, and fromSTARI patients from North Carolina, Missouri, and other states. Beforelinear discriminant analysis data were transformed by taking the log 2value and standardizing to the mean 0 and variance 1 within each MF.Samples were differentiated by healthy and STARI.

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TABLE 1 Classification modeling using the 261 molecular featurebiosignature list. RF (261 MFs) LASSO (38/82 MFs^(±)) Test-Set NumberNumber % Number % Classification Sample of Data Correctly ClassificationCorrectly Classification Model Group Files* Predicted Accuracy PredictedAccuracy 1: Two-Way Early Lyme 60 58 97 59 98 Model Disease STARI 38 3489 34 89 2: Three-Way Early Lyme 60 51 85 51 85 Model Disease STARI 3835 92 35 92 Healthy 40 38 95 37 93 Controls RF, random forest; LASSO,least absolute shrinkage and selection operator; MF; molecular feature.*Samples were analyzed in duplicate by LC-MS. ^(±)A total of 38 MFs wereselected by the LASSO model for two-way modeling and 82 MFs wereselected by the LASSO for three-way modeling.

TABLE 2 Serum samples used in the study Description of Sample SampleState Sample Samples Nos. Sample Criteria for Inclusion PurposeCollected Provider* Ref. Early Lyme Disease (n = 70) Age: 16-81 70 Atleast one EM present on Discovery NY NYMC (27) Male (52) initial visitto the clinic. Samples Training and Female (18) were collected atinitial visit to Test the clinic and pre-treatment. Positive cultureand/or PCR test for B. burgdorferi. Patients lived in an endemic areafor LD. STARI (n = 55) STARI Group 1 33 All patients had a physician-Discovery NC, VA, CDC, Fort (57) Age: 4-82 diagnosed erythema migrans-Training and GA, KY, Collins, Male (17) like rash ≥ 5 cm and a recentTest TN, AL, IA CO Female (16) history of possible or verified and NESTARI Group 2 22 exposure to Amblyomma MO NYMC (20) Age: 8-80 americanum(lone star) ticks Male (13) before the onset of symptoms. Female (9)Patients lived in a non-endemic area for LD with the exception of threepatients.^(±) Samples were standard two-tiered negative for LD. HealthyDonors (n = 95) Healthy Group 1 28 No history of tick-borne diseaseDiscovery CO CDC, Fort — Age: 18-unknown within the last 12 months andTraining and Collins, Male (8) lived in a non-endemic area for Test COFemale (20) LD. Samples were standard two-tiered negative for LD.Healthy Group 2^(†#) 30 No history of Lyme disease and NY NYMC — Age:18-74^(§) lived in an endemic area for LD. Samples were standard two-tiered negative for LD. Healthy Group 3^(†) 37 No previous diagnosiswith Verification^(¥) FL UCF (65) (65) Age: 18-60 and/or treated for LD;and could not have lived within the past 10 years in a state with a highincidence of LD (CT, DE, ME, MD, MA, MN, NH, NJ, NY, PA, VT, VA and Wl).Samples were standard two-tiered negative for LD. NYMC, New York MedicalCollege; CDC, Centers for Disease Control and Prevention; UCF,University of Central Florida; LD, Lime disease *Sample handling variedamong laboratories that provided samples. ^(±)Two patients were fromsouthwest Iowa and one was from southeast Virginia; both areas areconsidered to have low risk for Lyme disease and a higher prevalence ofA. americanum as compared to I. scapularis. ^(†)The gender of thesedonors was approximately 50% females and 50% males. ^(#)The samples wereobtained from the same geographic location as the early Lyme diseasesamples. ^(§)Age ranged from 18-74 for all donors (n = 100). Only asubset of 30 donors were used for this study. ^(¥)Healthy controls fromFlorida were used to verify that the dysregulation of MFs between EL andSTARI were not due to regional differences.

TABLE 3 261 MF biosignature list The experimentally obtained mass ofeach MF was used to search against the Metlin database and the HumanMetabolome Database (HMDB). The predicted chemical structures had tomatch to the MF mass within 15 ppm. MFs could have matches to multiplechemical structures of within the same classes of chemicals or tostructures of a different chemical class. The putative chemicalstructure data obtained by interrogation against the HMDB were used toevaluate possible metabolic pathways that differed between early Lymedisease and STARI patients (see Table 4). Compound Predicted # of m/zFormula Predicted Alternate Mass Chemical Structure Level Chemical 2 Way3 Way Retention (based on accurate Metabolite Class of Structures ± RFLASSO LASSO MF # Time mass) or Pathway Iden. 15 ppm Model Model ModelCSU/CDC- 166.0852 C₉H₁₁NO₂ Phenylalanine 1 >5 x x x 001 165.078Phenylalanine metabolism 1.86 CSU/CDC- 239.0919 C₁₂H₁₄O₅ Phenylpropanoid4 5 x x 002 238.0844 Trans-2,3,4- and 11.66 trimethoxycinnamatepolyketide metabolism CSU/CDC- 886.4296 — — 4 0 x x 003 1770.8438 12.18CSU/CDC- 181.0859 C₁₀H₁₂O₃ Endogenous 4 >5 x x 004 180.07885′-(3′-Methoxy-4′- metabolite 14.7 hydroxyphenyl)- associated gamma-with valerolactone microbiome CSU/CDC- 223.0968 C₁₂H₁₄O₄ — 4 >5 x 005222.0895 — 14.69 CSU/CDC- 286.1444 C₁₇H₁₉NO₃ Alkaloid 1 >5 x x 006285.1371 Piperine metabolism 16.08 CSU/CDC- 286.1437 C₁₇H₁₉NO₃ — 4 >5 x007 285.1364 — 16.06 CSU/CDC- 463.2339 C₂₅H₃₄O₈ Peptide 4 >5 x x 008462.2248 Ala Lys Met Asn 16.36 CSU/CDC- 242.2844 C₁₆H₃₅N — 4 1 x x 009241.2772 — 17.1 CSU/CDC- 1112.6727 — — 4 0 x 010 1111.6663 17.86CSU/CDC- 454.2923 C₂₁H₄₄NO₇P N-acyl 3 >5 x 011 453.2867 Glycerophospho-ethanolamine 18.08 N-Palmitoyl metabolism Ethanolamine CSU/CDC- 270.3156C₁₈H₃₉N — 4 1 x x x 012 269.3076 — 18.02 CSU/CDC- 284.3314 C₁₉H₄₁N — 4 1x x x 013 283.3236 — 18.13 CSU/CDC- 300.6407 C₃₃H₃₇N₅O₆ Peptide 4 >5 x xx 014 599.268 Asp Phe Arg Tyr 18.27 CSU/CDC- 522.3580 C₂₆H₅₂NO₇PGlycerophospholipid 3 >5 x 015 521.3483 PC(18:1) metabolism 18.5CSU/CDC- 363.2192 C₂₁H₃₀O₅ Sterol 4 >5 x 016 362.2132 4,5α- metabolism18.58 dihydrocortisone CSU/CDC- 590.4237 — — 4 0 x x 017 589.4194 19.24CSU/CDC- 388.3939 — — 4 0 x 018 387.3868 19.53 CSU/CDC- 300.2892C₁₈H₃₇NO₂ N-acyl 1 >5 x x x 019 299.2821 Palmitoyl ethanolamine 19.66ethanolamide metabolism CSU/CDC- 256.2632 C₁₆H₃₃NO Primary Fatty 1 1 x020 255.2561 Palmitic amide Acid Amide 20.08 Metabolism CSU/CDC-394.3515 — — 4 0 x 021 376.3171 20.09 CSU/CDC- 228.1955 — — 4 0 x 022227.1885 20.99 CSU/CDC- 284.2943 C₁₈H₃₇NO Primary Fatty 1 1 x 023283.2872 Stearamide Acid Amide 21.15 Metabolism CSU/CDC- 338.3430C₂₂H₄₃NO Primary Fatty 1 3 x 024 337.3344 13Z- Acid Amide 22.14Docosenamide Metabolism (Erucamide) CSU/CDC- 689.5604 C₃₈H₇₇N₂O₆PSphingolipid 3 >5 x 025 688.5504 SM(d18:1-15:0)/ metabolism 22.52 SM(d18:1/14:1-OH) CSU/CDC- 553.3904 C₃₅H₅₂O₅ Endogenous 4 3 x x 026552.3819 Furohyperforin metabolite - 23.38 derived from food CSU/CDC-432.2803 C₂₅H₃₇NO₅ Peptide 4 >5 x 027 431.2727 Ala Ile Lys Thr 10.8CSU/CDC- 389.2174 C₁₉H₃₂O₈ Fatty acid 4 >5 x x 028 388.2094 Methylmetabolism 15.47 10,12,13,15- bisepidioxy-16- hydroperoxy-8E-octadecenoate CSU/CDC- 385.2211 C₁₆H₂₈N₆O₅ Peptides 4 >5 x 029 384.2147Lys His Thr 15.84 CSU/CDC- 399.2364 — — 4 0 x x 030 398.2313 16.23CSU/CDC- 449.3261 C₄₆H₈₉NO₁₂S Sphingolipid 4 2 x 031 879.6122 C22-OHSulfatide metabolism 17.07 CSU/CDC- 467.3821 C₂₄H₄₀O₈ Prostaglandin 4 >5x 032 444.2717 2-glyceryl-6-keto- metabolism 17.1 PGF1α CSU/CDC-836.5936 C₄₄H₈₅NO₁₁S Sphingolipid 4 1 x 033 835.5845 C20 Sulfatidemetabolism 17.15 CSU/CDC- 792.5646 C₄₂H₈₂NO₁₀P Glycerophospholipid 4 >5x 034 791.5581 PS(36:0) metabolism 17.17 CSU/CDC- 356.2802 — — 4 0 x 035355.2722 17.35 CSU/CDC- 806.5798 C₄₃H₈₄NO₁₀P Glycerophospholipid 4 >5 x036 805.5746 PS(37:0) metabolism 17.71 CSU/CDC- 762.5582 C₄₁H₈₀NO₉PGlycerophospholipid 4 >5 x 037 761.5482 PS-O(35:1) metabolism 17.79CSU/CDC- 718.5308 C₃₉H₇₃O₈P Glycerophospholipid 4 >5 x 038 700.4946PA(36:2) metabolism 17.88 CSU/CDC- 734.5079 — — 4 0 x x x 039 1449.975317.81 CSU/CDC- 690.4825 — — 4 0 x 040 1361.924 17.95 CSU/CDC- 426.1798 —— 4 0 x 041 425.1725 18.03 CSU/CDC- 580.4144 — — 4 0 x x 042 1158.817318.26 CSU/CDC- 741.5154 C₈₃H₁₅₀O₁₇P₂ Glycerophospholipid 4 2 x 0431481.0142 CL(74:6) metabolism 18.24 CSU/CDC- 864.6245 C₄₆H₈₉NO₁₁SSphingolipid 4 2 x 044 863.6166 C22 Sulfatide metabolism 18.17 CSU/CDC-558.4017 — — 4 0 x 045 1080.7347 18.28 CSU/CDC- 719.5012 — — 4 0 x 0461402.9377 18.26 CSU/CDC- 536.3897 — — 4 0 x 047 1053.7382 18.36 CSU/CDC-538.8674 — — 4 0 x 048 1058.696 18.4 CSU/CDC- 653.4619 — — 4 0 x 0491270.8593 18.43 CSU/CDC- 732.5450 C₄₀H₇₅O₈P Glycerophospholipid 4 >5 x050 714.5092 PA(37:2) metabolism 18.47 CSU/CDC- 748.5232 — — 4 0 x 0511478.0059 18.58 CSU/CDC- 704.4985 — — 4 0 x x 052 1372.925 18.7 CSU/CDC-682.4841 — — 4 0 x 053 1328.9008 18.77 CSU/CDC- 360.3615 — — 4 0 x 054359.3555 18.89 CSU/CDC- 441.2412 C₂₀H₃₂N₄O₇ Peptide 4 >5 x 055 440.2325Pro Asp Pro Leu 19.09 CSU/CDC- 638.4554 — — 4 0 x 056 1240.847 18.92CSU/CDC- 755.5311 C₈₃H₁₄₄O₁₇P₂ Glycero- 4 2 x 057 1474.9941 CL(74:9)phospholipid 18.94 metabolism CSU/CDC- 711.5023 — — 4 0 x 058 1386.941719.09 CSU/CDC- 784.5530 — — 4 0 x 059 1567.0908 19.27 CSU/CDC- 645.4660— — 4 0 x 060 1271.8896 19.36 CSU/CDC- 623.4521 — — 4 0 x x 0611210.8362 19.55 CSU/CDC- 370.1837 C₁₉H₂₃N₅O₃ — 4 1 x x x 062 369.1757 —19.7 CSU/CDC- 300.2886 C₁₈H₃₄O₂ Fatty acid 4 >5 x 063 282.256913Z-octadecenoic metabolism 19.84 acid CSU/CDC- 309.0981 C₁₅H₁₆O₇ — 4 3x 064 308.0913 — 2.06 CSU/CDC- 561.2965 C₅₄H₈₈O₂₄ Endogenous 4 5 x 0651120.5778 Camellioside D metabolite - 11.7 derived from food CSU/CDC-811.1942 C₄₂H₃₀N₆O₁₂ — 4 1 x x x 066 810.1869 — 12.07 CSU/CDC- 947.7976C₆₂H₁₀₆O₆ Triacylglycerol 4 >5 x x x 067 946.7936 TAG(59:7) metabolism14.55 CSU/CDC- 1106.2625 — — 4 0 x 068 2209.5193 14.53 CSU/CDC- 371.2070C₁₅H₂₆N₆O₇ Peptide 4 >5 x 069 370.1997 His Ser Lys 15.52 CSU/CDC-389.2178 C₁₉H₃₂O₈ — 4 >5 x x 070 388.2099 — 15.52 CSU/CDC- 443.2649C₁₉H₃₄N₆O₆ Peptide 3 >5 x 071 442.256 Pro Gln Ala Lys 15.52 CSU/CDC-410.2033 — — 4 3 x x x 072 409.196 17.18 CSU/CDC- 850.6093 C₄₈H₈₄NO₉PGlycero- 4 1 x 073 849.6009 PS-O(42:6) phospholipid 17.63 metabolismCSU/CDC- 1111.6690 — — 4 0 x x 074 1110.6656 17.89 CSU/CDC- 1487.0005 —— 4 0 x x x 075 1485.9987 18.17 CSU/CDC- 697.4896 — — 4 0 x 076 1358.90918.32 CSU/CDC- 439.8234 — — 4 0 x 077 877.6325 18.71 CSU/CDC- 567.8897 —— 4 0 x 078 566.8818 18.73 CSU/CDC- 435.2506 C₂₁H₃₉O₇P Glycero- 4 >5 x079 434.243 Lyso-PA(18:2) phospholipid 19 metabolism CSU/CDC- 834.6136C₄₅H₈₈NO₁₀P Glycero- 4 >5 x 080 833.6057 PS(39:0) phospholipid 18.83metabolism CSU/CDC- 534.8834 — — 4 0 x 081 533.8771 18.82 CSU/CDC-468.8441 — — 4 0 x 082 467.8373 19.13 CSU/CDC- 482.4040 — — 4 0 x x 083481.3976 19.99 CSU/CDC- 533.1929 C₂₃H₂₈N₆O₉ Peptide 4 >5 x x 084532.1854 Asp His Phe Asp 20.84 CSU/CDC- 312.3259 — — 4 0 x 085 311.31922.05 CSU/CDC- 137.0463 C₄H₈O₅ Sugar 4 >5 x x x 086 136.0378 Threonatemetabolite 1.37 CSU/CDC- 466.3152 C₂₆H₄₃NO₆ Bile acid 1 3 x x 087465.3085 Glycocholic acid metabolism 14.73 CSU/CDC- 228.1955 — — 4 0 x088 227.1884 15.22 CSU/CDC- 385.2211 C₂₀H₃₂O₇ Peptide 4 >5 x 089384.2143 Lys His Thr 15.83 CSU/CDC- 403.2338 C₁₆H₃₀N₆O₆ Peptide 3 >5 x090 402.2253 Lys Gln Gln 15.84 CSU/CDC- 683.4728 — — 4 0 x x 0911347.9062 17.56 CSU/CDC- 675.4753 — — 4 0 x 092 1348.9377 18.37 CSU/CDC-682.4841 — — 4 0 x 093 1345.9257 18.76 CSU/CDC- 762.5401 — — 4 0 x 0941506.0367 19.36 CSU/CDC- 227.0897 C₉H₁₆O₅ — 4 2 x x 095 204.1002 — 9.68CSU/CDC- 189.1122 C₉H₁₄O₄ Fatty acid 1 >5 x 177 188.1049 NonanedioicAcid metabolism 12.27 CSU/CDC- 169.0860 C₉H₁₂O₃ Endogenous 4 >5 x 097168.0786 2,6-Dimethoxy-4- metabolite - 9.94 methylphenol derived fromfood CSU/CDC- 183.1016 C₁₀H₁₄O₃ — 4 >5 x x 098 182.0943 — 10.89 CSU/CDC-476.3055 C₂₆H₄₁N₃O₅ — 4 5 x x 099 475.2993 — 11.09 CSU/CDC- 276.1263C₁₅H₁₇NO₄ — 4 3 x 100 275.1196 — 11.16 CSU/CDC- 314.0672 C₁₀H₁₂N₅O₅P — 41 x 101 313.06 — 11.56 CSU/CDC- 201.1122 C₁₀H₁₆O₄ Fatty acid 3 >5 x 102200.1047 — metabolism 11.56 CSU/CDC- 115.0391 C₅H₆O₃ Phenylalanine 4 >5x 103 114.0318 — metabolism 11.57 CSU/CDC- 491.1569 C₂₄H₂₆O₁₁ — 4 >5 x104 490.1504 — 11.56 CSU/CDC- 241.1054 C₁₀H₁₈O₅ Fatty acid 4 3 x 105218.1157 3-Hydroxy- metabolism 11.57 sebacic acid CSU/CDC- 105.0914 — 40 x 106 104.0841 11.57 CSU/CDC- 811.7965 — — 4 0 x x x 107 810.788212.07 CSU/CDC- 311.1472 C₁₈H₂₀N₂O₄ Peptide 3 >5 x 108 328.1391 Phe Tyr12.22 CSU/CDC- 271.1543 — — 4 0 x 109 270.1464 12.24 CSU/CDC- 169.0860C₉H₁₂O₃ Endogenous 4 >5 x 110 168.0787 2,6-Dimethoxy-4- metabolite -12.24 methylphenol derived from food CSU/CDC- 187.0967 C₉H₁₄O₄ — 4 4 x111 186.0889 — 12.24 CSU/CDC- 215.1283 C₁₁H₁₈O₄ Endogenous 4 4 x x 112214.1209 alpha-Carboxy- metabolite - 12.32 delta-decalactone derivedfrom food CSU/CDC- 475.1635 C₂₅H₂₂N₄O₆ Peptide 4 >5 x 113 474.1547 HisCys Asp Thr 12.25 CSU/CDC- 129.0547 C₆H₈O₃ Fatty acid 4 >5 x 114128.0474 (4E)-2- metabolism 12.33 Oxohexenoic acid CSU/CDC- 519.1881C₂₀H₃₀N₄O₁₂ Poly D- 4 >5 x x 115 518.1813 Poly-g-D- glutamate 12.33glutamate metabolism CSU/CDC- 125.0599 C₇H₈O₂ Catechol 3 >5 x 116124.0527 4-Methylcatechol metabolism 13.12 CSU/CDC- 247.1550 C₁₂H₂₂O₅Fatty acid 4 4 x 117 246.1469 3-Hydroxy- metabolism 13.13 dodecanedioicacid CSU/CDC- 517.2614 C₂₁H₃₆N₆O₉ Peptide 4 >5 x 118 516.2544 Gln GluGln Ile 13.13 CSU/CDC- 301.0739 C₁₆H₁₂O₆ Endogenous 4 >5 x 119 300.0658Chrysoeriol metabolite - 13.14 derived from food CSU/CDC- 327.1773C₁₆H₂₄N₄O₂ — 4 1 x 120 304.1885 — 14.17 CSU/CDC- 387.2023 C₁₉H₃₀O₈Endogenous 4 >5 x 121 386.1935 Citroside A metabolite - 14.51 derivedfrom food CSU/CDC- 875.8451 — — 4 0 x 122 1749.684 14.55 CSU/CDC-737.5118 C₄₂H₇₃O₈P Glycero- 4 >5 x 123 736.5056 PA(39:5) phospholipid14.52 metabolism CSU/CDC- 1274.3497 — — 4 0 x 124 1273.3481 14.96CSU/CDC- 1274.2092 — — 4 0 x 125 1273.2 14.96 CSU/CDC- 1486.5728 — — 4 0x 126 2971.1328 14.95 CSU/CDC- 965.3818 — — 4 0 x 127 964.3727 15.37CSU/CDC- 1086.1800 — — 4 0 x x 128 2170.3435 15.38 CSU/CDC- 1086.0562C₉₇H₁₆₇N₅O₄₈ Sphingolipid 4 1 x 129 2170.0908 NeuAcalpha2- metabolism15.38 3Galbeta1- 3GalNAcbeta1- 4(9-OAc- NeuAcalpha2- 8NeuAcalpha2-3)Galbeta1- 4Glcbeta- Cer(d18:1/18:0) CSU/CDC- 1086.4344 — 4 0 x 1302169.8474 15.39 CSU/CDC- 1240.7800 — — 4 0 x 131 1239.7712 15.38CSU/CDC- 616.1776 — — 4 0 x x x 132 615.1699 15.43 CSU/CDC- 285.2061C₁₆H₂₈O₄ — 4 1 x x 133 284.1993 — 15.99 CSU/CDC- 357.1363 C₂₀H₂₀O₆Endogenous 4 >5 x x 134 356.1284 Xanthoxylol metabolite - 15.98 derivedfrom food CSU/CDC- 317.1956 C₁₂H₂₄N₆O₄ Peptide 4 >5 x 135 316.1885 ArgAla Ala 16.24 CSU/CDC- 299.1853 C₁₆H₂₆O₅ Prostaglandin 4 >5 x x 136298.1781 Tetranor-PGE1 metabolism 16.24 CSU/CDC- 334.2580 — — 4 0 x x137 333.2514 16.36 CSU/CDC- 317.2317 — — 4 0 x x 138 316.2254 16.63CSU/CDC- 299.2219 C₁₇H₃₀O₄ Fatty acid 4 2 x 139 298.2148 8E- metabolism16.64 Heptadecenedioic acid CSU/CDC- 748.5408 C₄₀H₇₈NO₉PGlycerophospholipid 4 >5 x 140 747.5317 PS-O(34:1) metabolism 17.23CSU/CDC- 331.2471 C₁₈H₃₄O₅ Fatty acid 4 >5 x x 141 330.2403 11,12,13-metabolism 17.26 trihydroxy-9- octadecenoic acid CSU/CDC- 712.4935C₇₉H₁₄0O₁₇P₂ Glycerophospholipid 4 1 x 142 1422.9749 CL(70:7) metabolism17.82 CSU/CDC- 674.5013 C₃₇H₇₂NO₇P Glycerophospholipid 4 >5 x 143673.4957 PE-P(32:1) metabolism 17.99 CSU/CDC- 583.3480 C₂₇H₄₆N₆O₈Peptide 4 1 x x 144 582.3379 Leu Lys Glu Pro 18.04 Pro CSU/CDC- 677.9537— — 4 0 x 145 676.9478 18.36 CSU/CDC- 531.3522 C₃₅H₄₆O₄ — 4 2 x 146530.3457 — 18.4 CSU/CDC- 585.2733 C₃₃H₃₆N₄O₆ Bilirubin 4 >5 x 147584.2649 15,16- breakdown 18.39 Dihydrobiliverdin products - Porphyrinmetabolism CSU/CDC- 513.3431 — — 4 0 x 148 512.3352 18.4 CSU/CDC-611.9156 — — 4 0 x 149 610.9073 18.59 CSU/CDC- 549.0538 — — 4 0 x 150531.0181 18.38 CSU/CDC- 755.5311 — — 4 0 x 151 1509.0457 18.93 CSU/CDC-713.4492 C₃₈H₆₅O₁₀P Glycerophospholipid 4 4 x x x 152 712.4391 PG(32:5)metabolism 19.35 CSU/CDC- 599.4146 C₄₀H₅₄O₄ Isoflavinoid 4 >5 x 153598.4079 Isomytiloxanthin 19.59 CSU/CDC- 762.5029 C₄₃H₇₂NO₈P Glycero-4 >5 x 154 761.4919 PE(38:7) phospholipid 19.66 metabolism CSU/CDC-502.3376 C₂₇H₄₀N₄O₄ Peptide 4 >5 x x x 155 484.3039 Gln Leu Pro Lys19.87 CSU/CDC- 741.4805 C₄₀H₆₉O₁₀P Glycero- 4 >5 x 156 740.4698 PG(34:5)phospholipid 19.96 metabolism CSU/CDC- 648.4672 C₃₄H₆₆NO₈P Glycero- 4 >5x x 157 647.4609 PE(29:1) phospholipid 19.98 metabolism CSU/CDC-415.3045 — — 4 0 x x x 158 414.2978 20.19 CSU/CDC- 516.3532 C₂₃H₄₂N₆O₆Peptide 4 1 x 159 498.3199 Ala Leu Ala Pro 20.27 Lys CSU/CDC- 769.5099C₄₂H₇₃O₁₀P Glycero- 4 >5 x 160 768.5018 PG(36:5) phospholipid 20.53metabolism CSU/CDC- 862.5881 — — 4 0 x 161 861.5818 20.86 CSU/CDC-837.5358 C₅₃H₇₂O₈ Endogenous 4 2 x 162 836.5274 Amitenone metabolite -21.11 derived from food CSU/CDC- 558.3995 C₂₆H₄₈N₆O₆ Peptide 4 2 x 163540.367 Leu Ala Pro Lys 21.44 Ile CSU/CDC- 366.3729 — — 4 0 x x x 164365.3655 22.79 CSU/CDC- 445.2880 C₄₅H₇₄O₁₅ Endogenous 4 1 x x 165854.5087 (3b,21b)-12- metabolite - 12.48 Oleanene- derived from3,21,28-triol 28- food [arabinosyl- (1−>3)-arabinosyl-(1−>3)-arabinoside] CSU/CDC- 333.1446 C₁₂H₂₀N₄O₇ Peptide 4 >5 x x x 166332.1373 Glu Gln Gly 12.89 CSU/CDC- 1105.9305 — — 4 0 x 167 2209.846214.53 CSU/CDC- 329.1049 C₁₈H₁₆O₆ Phenylalanine 4 >5 x 168 328.09762-Oxo-3- metabolism 14.61 phenylpropanoic acid CSU/CDC- 1241.2053 — — 40 x 169 1240.2 15.38 CSU/CDC- 1088.6731 — — 4 0 x 170 1087.6676 17.85CSU/CDC- 667.4391 C₃₇H₆₃O₈P Glycero- 4 >5 x 171 666.4323 PA(24:5)phospholipid 20.35 metabolism CSU/CDC- 133.0497 C₅H₈O₄ Pantothenate 4 >5x 172 132.0423 2-Acetolactic acid and CoA 11.57 Biosynthesis PathwayCSU/CDC- 259.1540 — — 4 0 x 173 258.1469 11.75 CSU/CDC- 311.1472C₁₀H₂₀N₆O₄ Dipeptide 4 >5 x 174 288.1574 Asn Arg 12.23 CSU/CDC- 147.0652C₆H₁₀O₄ Pantothenate 4 >5 x 175 146.0579 α-Ketopantoic and CoA 12.33acid Biosynthesis Pathway CSU/CDC- 169.0860 C₉H₁₂O₃ Endogenous 4 >5 x176 168.0788 Epoxyoxophorone metabolite - 12.29 derived from foodCSU/CDC- 187.0965 C₉H₁₄O₄ Endogenous 4 >5 x 096 186.08945-Butyltetrahydro- metabolite - 9.93 2-oxo-3- derived fromfurancarboxylic food acid CSU/CDC- 139.1116 C₉H₁₄O₄ Endogenous 4 >5 x178 138.1044 3,6-Nonadienal metabolite - 12.95 derived from foodCSU/CDC- 515.2811 C₂₆H₄₂O₁₀ Endogenous 4 >5 x 179 514.2745 Cofarylosidemetabolite - 13.14 derived from food CSU/CDC- 283.1522 C₂₅H₄₂N₂O₇SEndogenous 4 >5 x 180 282.1444 Epidihydrophaseic metabolite - 13.93 acidderived from food CSU/CDC- 1486.7386 — — 4 0 x x 181 2971.4668 14.97CSU/CDC- 285.2065 C₁₆H₂₈O₄ — 4 1 x x 182 284.1991 — 16.02 CSU/CDC-668.4686 C₁₆H₂₈O₄ Endogenous 4 1 x x 183 1317.8969 Omphalotin Ametabolite - 18.04 derived from food CSU/CDC- 454.2924 C₂₁H₄₁O₇PGlycero- 4 >5 x x 184 436.2587 Lyso-PA(18:1) phospholipid 18.1metabolism CSU/CDC- 706.9750 — — 4 0 x 185 705.9684 18.7 CSU/CDC-607.9324 — — 4 0 x x 186 606.9246 19.01 CSU/CDC- 834.5575 — — 4 0 x 187833.5502 20.32 CSU/CDC- 521.4202 — — 4 0 x x 188 503.3858 21.06 CSU/CDC-683.4727 — — 4 0 x 189 1364.9294 17.54 CSU/CDC- 728.9890 — — 4 1 x 1901455.9633 18.63 CSU/CDC- 726.5104 C₈₁H₁₄4O₁₇P₂ Glycero- 4 2 x 1911451.0035 CL(72:7) phospholipid 18.64 metabolism CSU/CDC- 633.9280 — — 40 x 192 632.9206 18.47 CSU/CDC- 176.0746 — — 4 0 x x 193 175.0667 2.31CSU/CDC- 596.9082 — — 4 0 x x 194 1191.8033 19.1 CSU/CDC- 209.0784C₁₇H₂₄O₃ Phenyl- 4 >5 x 195 208.0713 Benzylsuccinate propanoic acid 9.92metabolism CSU/CDC- 792.5483 — — 4 0 x 196 1566.055 18.46 CSU/CDC-618.9221 — — 4 0 x 197 1218.8083 19.02 CSU/CDC- 549.0543 — — 4 0 x 198531.0189 18.37 CSU/CDC- 553.7262 — — 4 0 x 199 552.7188 18.74 CSU/CDC-756.0320 — — 4 0 x 200 755.0266 18.95 CSU/CDC- 639.6307 — — 4 0 x 201638.6205 19.58 CSU/CDC- 753.4414 C₄₂H₆₇O₈P Glycerophospholipid 4 2 x 202730.4513 PA(39:8) metabolism 19.37 CSU/CDC- 532.5606 — — 4 0 x x 203531.5555 18.38 CSU/CDC- 279.1693 C₁₅H₂₂N₂O₃ Dipeptide 4 >5 x x 204278.1629 Phe Leu 11.05 CSU/CDC- 241.1069 C₁₂H₁₆O₅ Fatty acid 1 >5 x x x205 240.0996 3-Carboxy-4- metabolism 14.7 methyl-5-propyl-2-furanpropanoic acid (CMPF) CSU/CDC- 337.1667 C₁₂H₂₄N₄O₇ — 4 2 x x 206336.1599 — 20.67 CSU/CDC- 328.3204 C₂₀H₄₁NO₂ N-acyl 1 5 x 207 327.3148Stearoyl ethanolamine 20.72 ethanolamide metabolism CSU/CDC- 514.3718C₅₆H₉₉NO₁₄ Sphingolipid 4 1 x 208 1009.7122 3-O-acetyl- metabolism 18.42sphingosine- 2,3,4,6-tetra-O- acetyl- GalCer(d18:1/h22:0) CSU/CDC-630.4594 — — 4 0 x 209 1241.8737 19.95 CSU/CDC- 415.1634 C₈H₉N₅O₂Endogenous 4 2 x x 210 207.0784 6-Amino-9H- metabolite - 12.2 purine-9-derived from propanoic acid food CSU/CDC- 464.1916 C₁₆H₂₉N₇O₇S Peptide4 >5 x x x 211 463.1849 Arg Asp Cys Ala 13.05 CSU/CDC- 1249.2045 — — 4 0x x x 212 1248.1993 15.31 CSU/CDC- 1248.9178 — — 4 0 x x x 213 1247.914115.3 CSU/CDC- 244.2270 C₁₄H₂₉NO₂ N-acyl 4 3 x 214 243.22 Lauroylethanolamine 17.17 ethanolamide metabolism CSU/CDC- 463.3426 — — 4 0 x215 924.6699 18.08 CSU/CDC- 468.3892 C₃₁H₄₆O₂ — 4 1 x 216 450.3553 —19.17 CSU/CDC- 438.3787 — — 4 0 x 217 420.3453 420.3453 CSU/CDC-364.3407 — — 4 0 x x 218 346.3068 20.72 CSU/CDC- 158.1539 — — 4 0 x x x219 157.1466 15.36 CSU/CDC- 792.0006 — — 4 0 x 220 790.995 12.04CSU/CDC- 792.2025 — — 4 0 x 221 791.1947 12.04 CSU/CDC- 989.5004 — — 4 0x x 222 1976.9858 12.03 CSU/CDC- 791.6016 — — 4 0 x 223 790.594 12.04CSU/CDC- 819.6064 — — 4 0 x x 224 1635.8239 12.06 CSU/CDC- 1115.5593 — —4 0 x 225 2228.1028 14.95 CSU/CDC- 1486.9176 — — 4 0 x 226 2970.797614.96 CSU/CDC- 529.3381 C₂₄H₄₄N₆O₇ Peptide 4 5 x x x 227 528.3296 GlnVal Leu Leu 16.89 Gly CSU/CDC- 430.3161 C₂₃H₄₀O₆ — 4 1 x 228 412.2845 —20.23 CSU/CDC- 282.2776 C₁₈H₃₂O — 4 >5 x x x 229 264.2456 — 20.56CSU/CDC- 297.2793 C₁₉H₃₆O₂ Oleic acid 1 >5 x 230 296.2734 Methyl oleateester 20.66 CSU/CDC- 714.3655 — — 4 0 x 231 1426.718 11.73 CSU/CDC-714.5306 — — 4 0 x 232 1427.0479 11.76 CSU/CDC- 989.7499 — — 4 0 x 2331977.4865 12.03 CSU/CDC- 221.0744 C₇H₁₂N₂O₆ Peptide 4 >5 x 234 220.0672L-beta-aspartyl-L- 13.7 serine CSU/CDC- 190.1260 C₉H₁₉NOS 2- 4 2 x x x235 189.1187 8- oxocarboxylic 14.12 Methylthiooctanal acid doximemetabolism CSU/CDC- 313.2734 C₁₉H₃₆O₃ Fatty acid 4 5 x 236 312.26632-oxo- metabolism 18.91 nonadecanoic acid CSU/CDC- 286.2737 C₁₇H₃₅NO₂N-acyl 1 4 x x 237 285.2666 Pentadecanoyl ethanolamine 19.08ethanolamide metabolism CSU/CDC- 382.3675 C₂₄H₄₇NO₂ N-acyl 4 1 x x x 238381.3603 Erucicoyl ethanolamine 20.23 ethanolamide metabolism CSU/CDC-337.2712 C₁₉H₃₈O₃ Fatty acid 4 2 x 239 314.282 2-Hydroxy- metabolism20.66 nonadecanoic acid CSU/CDC- 441.3687 C₃₀H₄₈O₂ Sterol 4 >5 x 240440.3614 4,4-Dimethyl-14a- metabolism 21.26 formyl-5a- cholesta-8,24-dien-3b-ol CSU/CDC- 425.3735 C₃₀H₄₈O Sterol 4 >5 x 241 424.3666Butyrospermone metabolism 21.5 CSU/CDC- 356.3517 C₂₂H₄₅NO₂ N-acyl 1 2 x242 355.3448 Eicosanoyl ethanolamine 21.67 ethanolamide metabolismCSU/CDC- 393.2970 C₂₂H₄₂O₄ — 4 3 x 243 370.3082 — 22.46 CSU/CDC-477.2968 C₃₁H₄₀O₄ Peptide 4 >5 x x x 244 476.2898 Lys Lys Thr Thr 22.79CSU/CDC- 614.4833 — — 4 0 x x 245 613.4772 19.78 CSU/CDC- 167.9935C₇H₅NS₂ — 4 1 x 246 166.9861 — 13.2 CSU/CDC- 714.6967 — — 4 0 x x 2471427.3824 11.76 CSU/CDC- 459.3968 — — 4 0 x x x 248 458.3904 19.08CSU/CDC- 677.6170 C₄₇H₈₀O₂ Sterol 4 >5 x 249 676.6095 Cholesterol estermetabolism 20.71 (20:2) CSU/CDC- 298.2740 C₁₈H₃₅NO₂ Sphingolipid 2 >5 xx 250 297.2668 3-Ketospingosine metabolism 16.44 CSU/CDC- 460.2695C₂₆H₃₇NO₆ — 4 >5 x 251 459.2627 — 16.87 CSU/CDC- 1003.7020 — — 4 0 x x252 1002.696 18.46 CSU/CDC- 342.2635 C₁₉H₃₅NO₄ — 4 2 x x x 253 341.2565— 15.62 CSU/CDC- 529.3827 — — 4 0 x x x 254 1022.6938 17.86 CSU/CDC-630.4765 — — 4 0 x 255 612.4417 18.11 CSU/CDC- 514.3734 — — 4 0 x 2561026.7281 18.41 CSU/CDC- 667.4754 — — 4 0 x 257 1315.916 19.28 CSU/CDC-459.2502 C₂₃H₃₉O₇P Glycero- 2 >5 x x x 258 458.2429 Lyso PA(20:4)phospholipid 19.02 metabolism CSU/CDC- 516.8549 — — 4 0 x 259 1031.694518.43 CSU/CDC- 740.5242 C₈₃H₁₄₈O₁₇P₂ Glycero- 4 2 x 260 1479.0334CL(74:7) phospholipid metabolism CSU/CDC- 1104.0614 — — 4 0 x 2612206.1096 15.2

TABLE 4 MetaboAnalyst results Holm Pathway Hit Total Expected Hits Raw p-log(p) adjust FDR Impact Glycerophospholipid 39 1.2638 4^(†) 0.0355453.337 1 1 0.33235 metabolism Sphingolipid 25 0.81014 3^(±) 0.0451073.0987 1 1 0.15499 metabolism Valine, leucine and 27 0.87495 2 0.217241.5268 1 1 0.17117 isoleucine biosynthesis Phenylalanine 45 1.4582 10.77605 0.25353 1 1 0.11906 metabolism alpha-Linolenic acid 29 0.93976 20.24148 1.421 1 1 0 metabolism Glycosylphosphatidylino 14 0.45368 10.37027 0.99353 1 1 0.0439 sitol(GP1)-anchor biosynthesis Linoleic acid15 0.48608 1 0.39079 0.93957 1 1 0 metabolism Riboflavin metabolism 210.68052 1 0.50079 0.69157 1 1 0 Phenylalanine, tyrosine and tryptophan27 0.87495 1 0.59113 0.52572 1 1 0.00062 biosynthesis Pantothenate andCoA 27 0.87495 1 0.59113 0.52572 1 1 0.02002 biosynthesis Steroidhormone 99 3.2081 3 0.63116 0.4602 1 1 0.0382 biosynthesis Glycerolipidmetabolism 32 1.037 1 0.65393 0.42476 1 1 0.01247 Ubiquinone and other36 1.1666 1 0.69723 0.36064 1 1 0 terpenoid-quinone bios+A14:129ynthesisNitrogen metabolism 39 1.2638 1 0.72615 0.32 1 1 0 Butanoate metabolism40 1.2962 1 0.73517 0.30766 1 1 0.04772 Ascorbate and aldarate 45 1.45821 0.77605 0.25353 1 1 0.00802 metabolism Drug metabolism - 86 2.7869 20.77721 0.25205 1 1 0.0176 cytochrome P450 Primary bile acid 47 1.5231 10.7906 0.23496 1 1 0.00846 biosynthesis Lysine degradation 47 1.5231 10.7906 0.23496 1 1 0.06505 Fatty acid biosynthesis 49 1.5879 1 0.804220.21788 1 1 0 Fatty acid metabolism 50 1.6203 1 0.81069 0.20986 1 1 0Starch and sucrose 50 1.6203 1 0.81069 0.20986 1 1 0.01265 metabolismPentose and 53 1.7175 1 0.82888 0.18768 1 1 0.009 glucuronateinterconversions Arachidonic acid 62 2.0091 1 0.87371 0.135 1 1 0metabolism Aminoacyl-tRNA 75 2.4304 1 0.91874 0.084752 1 1 0biosynthesis Purine metabolism 92 2.9813 1 0.95452 0.046547 1 1 0.00791Porphyrin and 104 3.3702 1 0.96989 0.030577 1 1 0.01101 chlorophyllmetabolism Total, the total number of compounds in the pathway; Hits,the actual number of compounds in the pathway matched from the 261 MFbiosignature list; Raw p, the original p value calculated from theenrichment analysis; Holm adjust, the adjusted p value by theHolm-Bonferroni method; FDR, the p value adjusted using False DiscoveryRate; Impact, the pathway impact value calculated from pathway topologyanalysis. The MetaboAnalyst results were used to target specific MFs inthe early Lyme disease-STARI biosignature for structural identification.^(†)The 4 hits in the glycerophospholipid metabolism pathway werephosphatidic acid, phosphatidylethanolamine, phosphatidylcholine andlysophosphotidylcholine. ^(±)The 3 hits in the sphingolipid metabolismpathway were in sphingosine, dehydrosphinganine and sulfatide.

TABLE 5 Regression coefficients (β) of the LASSO two-way statisticalmodel MF Id Coefficient MF Id Coefficient Intercept −0.5089 CSU/CDC-166−0.2033 CSU/CDC-001 −0.3032 CSU/CDC-182 −0.1077 CSU/CDC-002 −0.0359CSU/CDC-204 −0.163 CSU/CDC-012 −0.31 CSU/CDC-205 −0.8688 CSU/CDC-013−0.2256 CSU/CDC-211 0.43327 CSU/CDC-014 0.05737 CSU/CDC-212 −0.3513CSU/CDC-028 0.21447 CSU/CDC-213 −0.422 CSU/CDC-039 0.29641 CSU/CDC-2191.01872 CSU/CDC-062 0.0152 CSU/CDC-227 0.43588 CSU/CDC-066 −0.0559CSU/CDC-229 0.11674 CSU/CDC-067 0.63951 CSU/CDC-235 0.3664 CSU/CDC-072−0.1451 CSU/CDC-019 0.52461 CSU/CDC-075 0.10409 CSU/CDC-238 0.7812CSU/CDC-086 0.71497 CSU/CDC-244 −0.7325 CSU/CDC-107 −0.2586 CSU/CDC-2470.00621 CSU/CDC-132 0.88577 CSU/CDC-248 0.38858 CSU/CDC-152 −0.6125CSU/CDC-253 0.10575 CSU/CDC-155 −0.0083 CSU/CDC-254 0.27792 CSU/CDC-158−0.027 CSU/CDC-258 −0.5593 CSU/CDC-164 0.22005 The regressioncoefficient for each of the 38 MFs (CSU/CDC-#) used in the LASSO two-wayclassification model are provided. The regression coefficients weregenerated with data from the Training-Set samples, and applied in theclassification of the Test-Set samples as shown in Table 6.

TABLE 6 LASSO and RF two-way model classification probability scores TheLASSO and RF probability scores are provided for each patient sampletested in duplicate. These are probability scores for the Test-Setsamples. A probability score of ≥0.5 classified the samples as earlyLyme disease (EL), and a probability score of <0.5 resulted in thesample being classified as STARI. LASSO RF Coded Probability LASSOProbability RF Patient Sample ID Score Classification ScoreClassification Sample ID Type Valb1618 0.9979 EL 0.8980 EL EDL134-022315EL Valb1591 0.9995 EL 0.8980 EL EDL134-120214 EL Valb1454 0.9900 EL0.6320 EL EDL135-022315 EL Valb0820 0.5264 EL 0.8660 EL EDL135-120214 ELValb0989 0.9820 EL 0.8620 EL EDL136-022315 EL Valb0546 0.8814 EL 0.8960EL EDL136-120214 EL Valb1573 0.9875 EL 0.5840 EL EDL137-022315 ELValb1299 0.7198 EL 0.4380 STARI EDL137-120214 EL Valb0477 0.9247 EL0.7780 EL EDL138-022315 EL Valb0160 0.9868 EL 0.9160 EL EDL138-120214 ELValb0813 0.7300 EL 0.4880 STARI EDL139-022315 EL Valb0443 0.8307 EL0.7680 EL EDL139-120214 EL Valb1412 0.9287 EL 0.7200 EL EDL140-022315 ELValb0886 0.9045 EL 0.8140 EL EDL140-120214 EL Valb0827 0.9846 EL 0.9040EL EDL71-022315 EL Valb0186 0.9609 EL 0.9180 EL EDL71-120214 EL Valb13370.9417 EL 0.8200 EL EDL73-022315 EL Valb0714 0.9836 EL 0.9000 ELEDL73-120214 EL Valb1510 0.9773 EL 0.7720 EL EDL74-022315 EL Valb06420.9986 EL 0.8520 EL EDL74-120214 EL Valb1586 0.9995 EL 0.9020 ELEDL75-022315 EL Valb1402 1.0000 EL 0.9160 EL EDL75-120214 EL Valb05930.9595 EL 0.8020 EL EDL76-022315 EL Valb0608 0.6940 EL 0.7980 ELEDL76-120214 EL Valb0808 0.9205 EL 0.8720 EL EDL77-022315 EL Valb07500.9998 EL 0.7240 EL EDL77-120214 EL Valb0907 0.9459 EL 0.6720 ELEDL78-022315 EL Valb0585 0.9891 EL 0.9180 EL EDL78-120214 EL Valb16380.9832 EL 0.6000 EL EDL79-022315 EL Valb1640 0.9906 EL 0.8500 ELEDL79-120214 EL Valb1430 0.9812 EL 0.7580 EL ELL06-022315 EL Valb11550.9995 EL 0.8080 EL ELL06-120214 EL Valb1553 0.9783 EL 0.7780 ELELL07-022315 EL Valb1562 0.9999 EL 0.7920 EL ELL07-120214 EL Valb14450.8085 EL 0.7160 EL ELL08-022315 EL Valb1188 0.9983 EL 0.7860 ELELL08-120214 EL Valb1613 0.9993 EL 0.8640 EL ELL09-022315 EL Valb15141.0000 EL 0.8820 EL ELL09-120214 EL Valb1479 0.3775 STARI 0.6320 ELELL10-022315 EL Valb0933 0.9095 EL 0.8380 EL ELL10-120214 EL Valb09230.7083 EL 0.8120 EL ELL16-022315 EL Valb0338 0.7215 EL 0.8320 ELELL16-120214 EL Valb0783 0.7849 EL 0.8880 EL ELL17-022315 EL Valb02610.9862 EL 0.9120 EL ELL17-120214 EL Valb1264 0.9418 EL 0.8240 ELELL18-022315 EL Valb0545 0.9738 EL 0.8480 EL ELL18-120214 EL Valb14270.9704 EL 0.8480 EL ELL61-022315 EL Valb1071 0.9664 EL 0.7620 ELELL61-120214 EL Valb1211 0.7950 EL 0.7360 EL ELL62-022315 EL Valb12170.7831 EL 0.8360 EL ELL62-120214 EL Valb1414 0.9892 EL 0.9100 ELELL63-022315 EL Valb1104 0.9699 EL 0.8600 EL ELL63-120214 EL Valb07360.9469 EL 0.9300 EL ELL64-022315 EL Valb0384 0.9780 EL 0.9040 ELELL64-120214 EL Valb0672 0.9415 EL 0.7680 EL ELL65-022315 EL Valb03000.9927 EL 0.8920 EL ELL65-120214 EL Valb1018 0.9093 EL 0.8320 ELELL66-022315 EL Valb0458 0.8905 EL 0.8480 EL ELL66-120214 EL Valb13560.9174 EL 0.8100 EL ELL67-022315 EL Valb0492 0.9747 EL 0.7260 ELELL67-120214 EL Valb1561 0.0313 STARI 0.4860 STARI M06A-022315 STARIValb1328 0.8608 EL 0.6060 EL M06A-120214 STARI Valb0329 0.1613 STARI0.2680 STARI M09A-022315 STARI Valb0070 0.2476 STARI 0.4080 STARIM09A-120214 STARI Valb1052 0.0242 STARI 0.4060 STARI M13A-022315 STARIValb0809 0.8461 EL 0.8340 EL M13A-120214B STARI Valb1256 0.0157 STARI0.2900 STARI M16A-022315 STARI Valb1100 0.3798 STARI 0.4120 STARIM16A-120214 STARI Valb1236 0.2314 STARI 0.6800 EL M19A-022315 STARIValb0580 0.5508 EL 0.6140 EL M19A-120214 STARI Valb1525 0.7045 EL 0.4720STARI M22A-022315 STARI Valb0534 0.0496 STARI 0.4580 STARI M22A-120214STARI Valb0556 0.1448 STARI 0.3400 STARI M26A-022315 STARI Valb01160.4234 STARI 0.2860 STARI M26A-120214 STARI Valb0461 0.0037 STARI 0.2360STARI M27A-022315 STARI Valb0266 0.1015 STARI 0.2080 STARI M27A-120214STARI Valb0447 0.0316 STARI 0.1220 STARI S03-022315 STARI Valb00260.0060 STARI 0.1420 STARI S03-120214 STARI Valb1114 0.0010 STARI 0.1760STARI S09-022315 STARI Valb0464 0.0254 STARI 0.2120 STARI S09-120214STARI Valb1292 0.0004 STARI 0.1280 STARI S17-022315 STARI Valb07540.0005 STARI 0.1020 STARI S17-120214 STARI Valb0434 0.0257 STARI 0.2520STARI S21-022315 STARI Valb0044 0.0559 STARI 0.4300 STARI S21-120214STARI Valb0873 0.0173 STARI 0.1840 STARI S33-022315 STARI Valb03520.0012 STARI 0.2200 STARI S33-120214 STARI Valb1141 0.0001 STARI 0.1120STARI S39-022315 STARI Valb0480 0.0002 STARI 0.1160 STARI S39-120214STARI Valb0618 0.0158 STARI 0.3220 STARI S43-022315 STARI Valb06600.1493 STARI 0.3020 STARI S43-120214 STARI Valb0223 0.0007 STARI 0.0960STARI S47-022315 STARI Valb0054 0.0095 STARI 0.0940 STARI S47-120214STARI Valb0335 0.0093 STARI 0.0660 STARI S53-022315 STARI Valb01970.0183 STARI 0.0360 STARI S53-120214 STARI Valb0409 0.2080 STARI 0.2080STARI S55-022315 STARI Valb0060 0.0332 STARI 0.1280 STARI S55-120214STARI Valb0437 0.0004 STARI 0.0980 STARI S65-022315 STARI Valb00930.0003 STARI 0.1500 STARI S65-120214 STARI

TABLE 7 Regression coefficients (β) of the LASSO three-way statisticalmodel. MF Id Early Lyme Disease Healthy Controls STARI Intercept 0.5755−0.4927 −0.0828 CSU/CDC-001 0.37556 0 0 CSU/CDC-003 0 0.4377 0CSU/CDC-004 0 0.00298 0 CSU/CDC-006 0.0704 0 0 CSU/CDC-008 −0.1193 0 0CSU/CDC-009 0.22921 0 0 CSU/CDC-012 0.15307 0 −0.2457 CSU/CDC-013 0 0−0.1007 CSU/CDC-014 0 0 0.72128 CSU/CDC-017 0.11117 0 0 CSU/CDC-026 0−0.0633 0.05925 CSU/CDC-030 0 0.05795 0 CSU/CDC-039 0 −0.6065 0.06517CSU/CDC-042 −0.4151 0.02856 0 CSU/CDC-052 0 0.05484 0 CSU/CDC-061 00.08714 0 CSU/CDC-062 0 0 0.60672 CSU/CDC-066 0 0 −0.3676 CSU/CDC-067−1.1528 0 0 CSU/CDC-070 −0.5929 0.5531 0 CSU/CDC-072 0 0 −0.0857CSU/CDC-074 0.01711 0 0 CSU/CDC-075 0 0 0.18553 CSU/CDC-083 0 −0.0872 0CSU/CDC-084 0 −0.2013 0.21541 CSU/CDC-086 −1.1622 0 0.06776 CSU/CDC-0870 0.03553 0 CSU/CDC-091 0 −0.6683 0 CSU/CDC-095 0 0 −0.0694 CSU/CDC-0980 0.05396 0 CSU/CDC-099 0 −0.0398 0 CSU/CDC-107 0.36836 0 −0.1847CSU/CDC-112 0 1.11724 0 CSU/CDC-115 0 0.12435 0 CSU/CDC-128 0 0.4206−0.1927 CSU/CDC-132 0 0 1.0998 CSU/CDC-133 0.35613 −0.1349 0 CSU/CDC-1340 −0.1009 0 CSU/CDC-136 0 −1.2108 0 CSU/CDC-137 0 −0.2512 0 CSU/CDC-138−0.0183 0 0 CSU/CDC-141 0 0 0.2233 CSU/CDC-144 0 −0.1318 0 CSU/CDC-1520.70277 0 0 CSU/CDC-155 0.27512 0 0 CSU/CDC-157 0 0 0.0505 CSU/CDC-158 01.89865 0 CSU/CDC-164 −0.2964 0 0 CSU/CDC-165 0 −0.4008 0 CSU/CDC-1660.14382 0 0 CSU/CDC-181 0 1.3044 0 CSU/CDC-183 0 −0.7613 0.01014CSU/CDC-184 0.35021 0 0 CSU/CDC-186 0 0.40861 0 CSU/CDC-188 0 0.5533 0CSU/CDC-193 0 −1.2355 0 CSU/CDC-194 0 0.57412 0 CSU/CDC-203 −0.0308 0 0CSU/CDC-205 0.50193 0 −0.3139 CSU/CDC-206 0 −0.0668 0 CSU/CDC-210 0 0−0.218 CSU/CDC-211 −0.7208 0 0.20891 CSU/CDC-212 0 0 −0.0139 CSU/CDC-2130 0 −0.2463 CSU/CDC-218 0 0.00722 0 CSU/CDC-219 −1.0252 0 0 CSU/CDC-2220 −0.4632 0 CSU/CDC-224 0 −0.516 0 CSU/CDC-227 −0.4157 0 0.59261CSU/CDC-229 0 0 0.86651 CSU/CDC-235 −0.9905 0 0 CSU/CDC-019 0 −0.03260.52245 CSU/CDC-237 0 0.62355 0 CSU/CDC-238 0 0 0.96539 CSU/CDC-2441.5845 0 0 CSU/CDC-245 0 −1.3521 0 CSU/CDC-248 −0.0904 0 0.06017CSU/CDC-250 0 0 −0.0882 CSU/CDC-252 0 −0.0646 0 CSU/CDC-253 0 0 0.16563CSU/CDC-254 −0.1985 0 0 CSU/CDC-258 0 0 −0.7011 The regressioncoefficient for each of the 82 MFs (CSU/CDC-#) used in the LASSOthree-way classification model are provided. The regression coefficientswere generated with data from the Training-Set samples, and applied inthe classification of the Test-Set samples as shown in Table 8.

TABLE 8 LASSO and RF three-way model classification probability scores.The LASSO and RF probability scores are provided for each patient sampletested in duplicate. These are probability scores for the Test-Setsamples. Both the LASSO and RF classifiers provided a probability scorefor a sample being early Lyme disease patient (EL), healthy control (HC)and STARI. The sample was classified based on the highest probabilityscore for membership in one of the three groups (EL, HC, or STARI).LASSO Probability RF Probability Coded Score for EL, HC, LASSO Score forEL, HC, RF Sample Patient Sample ID and STARI Classification and STARIClassification ID Type Valb1618 0.9998 EL 0.8420 EL EDL134- EL 0.00000.0560 022315 0.0002 0.1020 Valb1591 1.0000 EL 0.8600 EL EDL134- EL0.0000 0.0320 120214 0.0000 0.1080 Valb1454 0.9978 EL 0.5140 EL EDL135-EL 0.0003 0.0840 022315 0.0019 0.4020 Valb0820 0.9798 EL 0.6560 ELEDL135- EL 0.0010 0.1140 120214 0.0192 0.2300 Valb0989 0.9765 EL 0.3620HC EDL136- EL 0.0190 0.5660 022315 0.0045 0.0720 Valb0546 0.9184 EL0.5760 EL EDL136- EL 0.0198 0.3360 120214 0.0618 0.0880 Valb1573 0.6006EL 0.4640 EL EDL137- EL 0.3980 0.1620 022315 0.0015 0.3740 Valb12990.0350 STARI 0.4640 EL EDL137- EL 0.0012 0.1380 120214 0.9639 0.3980Valb0477 0.9823 EL 0.5760 EL EDL138- EL 0.0001 0.2480 022315 0.01750.1760 Valb0160 0.9570 EL 0.5800 EL EDL138- EL 0.0284 0.3560 1202140.0146 0.0640 Valb0813 0.7815 EL 0.3380 EL EDL139- EL 0.1288 0.3340022315 0.0897 0.3280 Valb0443 0.1403 HC 0.5140 EL EDL139- EL 0.85500.3480 120214 0.0047 0.1380 Valb1412 0.9258 EL 0.5260 EL EDL140- EL0.0010 0.1860 022315 0.0732 0.2880 Valb0886 0.6301 EL 0.4060 HC EDL140-EL 0.1495 0.4380 120214 0.2204 0.1560 Valb0827 0.9395 EL 0.5600 ELEDL71- EL 0.0600 0.3240 022315 0.0005 0.1160 Valb0186 0.9623 EL 0.5460EL EDL71- EL 0.0341 0.3980 120214 0.0036 0.0560 Valb1337 0.9873 EL0.6840 EL EDL73- EL 0.0000 0.0480 022315 0.0127 0.2680 Valb0714 0.9991EL 0.7480 EL EDL73- EL 0.0000 0.0740 120214 0.0009 0.1780 Valb15100.9795 EL 0.6700 EL EDL74- EL 0.0000 0.1140 022315 0.0205 0.2160Valb0642 0.9990 EL 0.7280 EL EDL74- EL 0.1080 120214 0.1640 Valb15861.0000 EL 0.8180 EL EDL75- EL 0.0000 0.0920 022315 0.0000 0.0900Valb1402 1.0000 EL 0.8460 EL EDL75- EL 0.0000 0.0640 120214 0.00000.0900 Valb0593 0.9699 EL 0.5380 EL EDL76- EL 0.0155 0.3180 0223150.0146 0.1440 Valb0608 0.2554 HC 0.4000 HC EDL76- EL 0.4250 0.4320120214 0.3197 0.1680 Valb0808 0.9747 EL 0.5080 EL EDL77- EL 0.01350.3480 022315 0.0118 0.1440 Valb0750 1.0000 EL 0.5600 EL EDL77- EL0.0000 0.2140 120214 0.0000 0.2260 Valb0907 0.9570 EL 0.5640 EL EDL78-EL 0.0006 0.1900 022315 0.0424 0.2460 Valb0585 0.8967 EL 0.5760 ELEDL78- EL 0.0837 0.3440 120214 0.0196 0.0800 Valb1638 0.9978 EL 0.5880EL EDL79- EL 0.0000 0.0940 022315 0.0022 0.3180 Valb1640 0.9891 EL0.8180 EL EDL79- EL 0.0000 0.0700 120214 0.0109 0.1120 Valb1430 0.9960EL 0.6740 EL ELL06- EL 0.0000 0.0980 022315 0.0040 0.2280 Valb11550.9921 EL 0.7140 EL ELL06- EL 0.0073 0.1020 120214 0.0006 0.1840Valb1553 0.9522 EL 0.4940 EL ELL07- EL 0.0308 0.3240 022315 0.01700.1820 Valb1562 0.9989 EL 0.6360 EL ELL07- EL 0.0011 0.1900 1202140.0000 0.1740 Valb1445 0.8847 EL 0.6300 EL ELL08- EL 0.0032 0.1880022315 0.1122 0.1820 Valb1188 0.9871 EL 0.6260 EL ELL08- EL 0.01240.1600 120214 0.0005 0.2140 Valb1613 1.0000 EL 0.8320 EL ELL09- EL0.0000 0.0740 022315 0.0000 0.0940 Valb1514 1.0000 EL 0.7780 EL ELL09-EL 0.0000 0.1120 120214 0.0000 0.1100 Valb1479 0.2786 STARI 0.5340 ELELL10- EL 0.1610 0.2020 022315 0.5604 0.2640 Valb0933 0.5295 EL 0.6060EL ELL10- EL 0.3586 0.2880 120214 0.1119 0.1060 Valb0923 0.6352 EL0.5600 EL ELL16- EL 0.1147 0.2900 022315 0.2501 0.1500 Valb0338 0.4277STARI 0.4760 EL ELL16- EL 0.0788 0.4300 120214 0.4935 0.0940 Valb07830.8276 EL 0.5720 EL ELL17- EL 0.0090 0.3660 022315 0.1634 0.0620Valb0261 0.9899 EL 0.6060 EL ELL17- EL 0.0038 0.3060 120214 0.00640.0880 Valb1264 0.7738 EL 0.5880 EL ELL18- EL 0.0116 0.2880 0223150.2146 0.1240 Valb0545 0.1309 HC 0.5000 EL ELL18- EL 0.8465 0.3480120214 0.0225 0.1520 Valb1427 0.9965 EL 0.5460 EL ELL61- EL 0.00220.3180 022315 0.0012 0.1360 Valb1071 0.9949 EL 0.5240 EL ELL61- EL0.0040 0.3060 120214 0.0011 0.1700 Valb1211 0.6844 EL 0.4780 EL ELL62-EL 0.3003 0.3280 022315 0.0153 0.1940 Valb1217 0.0136 HC 0.4560 ELELL62- EL 0.9855 0.4140 120214 0.0009 0.1300 Valb1414 0.9456 EL 0.6260EL ELL63- EL 0.0523 0.2680 022315 0.0020 0.1060 Valb1104 0.4263 HC0.4460 HC ELL63- EL 0.5711 0.4700 120214 0.0026 0.0840 Valb0736 0.8514EL 0.4700 HC ELL64- EL 0.1341 0.4880 022315 0.0145 0.0420 Valb03840.7501 EL 0.4000 HC ELL64- EL 0.2400 0.5680 120214 0.0100 0.0320Valb0672 0.9502 EL 0.4200 HC ELL65- EL 0.0479 0.4660 022315 0.00190.1140 Valb0300 0.9441 EL 0.5220 EL ELL65- EL 0.4020 120214 0.0760Valb1018 0.2340 HC 0.3360 HC ELL66- EL 0.7645 0.6140 022315 0.00150.0500 Valb0458 0.5250 EL 0.2980 HC ELL66- EL 0.4676 0.6620 1202140.0074 0.0400 Valb1356 0.6663 EL 0.6480 EL ELL67- EL 0.3313 0.1860022315 0.0024 0.1660 Valb0492 0.7816 EL 0.5200 EL ELL67- EL 0.21690.3160 120214 0.0015 0.1640 Valb0408 0.0012 HC 0.0840 HC HCN07- HC0.9984 0.8860 022315 0.0004 0.0300 Valb0311 0.0039 HC 0.0720 HC HCN07-HC 0.9653 0.8880 120214 0.0308 0.0400 Valb0440 0.0006 HC 0.1480 HCHCN08- HC 0.9993 0.8140 022315 0.0001 0.0380 Valb0123 0.0189 HC 0.1960HC HCN08- HC 0.9758 0.7700 120214 0.0053 0.0340 Valb0327 0.0029 HC0.1180 HC HCN09- HC 0.9970 0.8600 022315 0.0001 0.0220 Valb0112 0.0000HC 0.0540 HC HCN09- HC 0.9995 0.9260 120214 0.0005 0.0200 Valb11080.0042 HC 0.3780 HC HCN16- HC 0.9957 0.5120 022315 0.0001 0.1100Valb0269 0.0724 HC 0.0700 HC HCN16- HC 0.9238 0.9120 120214 0.00390.0180 Valb0411 0.0243 HC 0.2760 HC HCN17- HC 0.9710 0.6700 0223150.0047 0.0540 Valb0029 0.0491 HC 0.0620 HC HCN17- HC 0.9435 0.9220120214 0.0074 0.0160 Valb0860 0.1211 HC 0.3560 HC HCN18- HC 0.85400.4300 022315 0.0250 0.2140 Valb0302 0.0198 HC 0.0240 HC HCN18- HC0.9792 0.9720 120214 0.0010 0.0040 Valb0709 0.0060 HC 0.2980 HC HCN19-HC 0.9930 0.5740 022315 0.0010 0.1280 Valb0178 0.0024 HC 0.0480 HCHCN19- HC 0.9940 0.9260 120214 0.0036 0.0260 Valb0962 0.0978 HC 0.3700HC HCN25- HC 0.8543 0.4420 022315 0.0479 0.1880 Valb0418 0.6988 EL0.2500 HC HCN25- HC 0.1304 0.5540 120214 0.1708 0.1960 Valb0632 0.0014HC 0.1080 HC HCN28- HC 0.9982 0.8440 022315 0.0005 0.0480 Valb01240.0226 HC 0.0800 HC HCN28- HC 0.9655 0.8780 120214 0.0119 0.0420Valb0690 0.9013 EL 0.5920 EL HCN29- HC 0.0929 0.3340 022315 0.00580.0740 Valb0066 0.0876 HC 0.1260 HC HCN29- HC 0.8866 0.8560 1202140.0257 0.0180 Valb1466 0.0038 HC 0.1860 HC HCW13- HC 0.9957 0.7800022315 0.0005 0.0340 Valb0777 0.2406 HC 0.1320 HC HCW13- HC 0.75400.8560 120214 0.0054 0.0120 Valb1405 0.0021 HC 0.2540 HC HCW21- HC0.9959 0.5900 022315 0.0019 0.1560 Valb0802 0.2993 HC 0.1660 HC HCW21-HC 0.6973 0.8180 120214 0.0034 0.0160 Valb1254 0.5258 EL 0.4020 ELHCW25- HC 0.4539 0.3720 022315 0.0203 0.2260 Valb0697 0.0064 HC 0.4060HC HCW25- HC 0.9906 0.4180 120214 0.0031 0.1760 Valb1138 0.0005 HC0.1720 HC HCW26- HC 0.9988 0.7260 022315 0.0007 0.1020 Valb0520 0.0041HC 0.1580 HC HCW26- HC 0.9956 0.7940 120214 0.0004 0.0480 Valb11190.0001 HC 0.2120 HC HCW28- HC 0.9998 0.7240 022315 0.0001 0.0640Valb0572 0.1165 HC 0.1180 HC HCW28- HC 0.8831 0.8600 120214 0.00040.0220 Valb0943 0.0616 HC 0.2260 HC HCW29- HC 0.9320 0.5440 0223150.0064 0.2300 Valb0419 0.3990 HC 0.2480 HC HCW29- HC 0.5992 0.6840120214 0.0018 0.0680 Valb1282 0.0191 HC 0.2980 HC HCW34- HC 0.60250.4380 022315 0.3783 0.2640 Valb0719 0.0209 HC 0.0980 HC HCW34- HC0.9768 0.8980 120214 0.0024 0.0040 Valb1535 0.0056 HC 0.2160 HC HCW37-HC 0.9885 0.5380 022315 0.0059 0.2460 Valb1091 0.0163 HC 0.2120 HCHCW37- HC 0.9766 0.7280 120214 0.0071 0.0600 Valb1509 0.1004 HC 0.3080HC HCW44- HC 0.8845 0.5860 022315 0.0151 0.1060 Valb0944 0.0532 HC0.2300 HC HCW44- HC 0.9143 0.7280 120214 0.0325 0.0420 Valb1349 0.0037HC 0.3080 HC HCW46- HC 0.9898 0.6100 022315 0.0066 0.0820 Valb08010.0039 HC 0.2640 HC HCW46- HC 0.9822 0.6500 120214 0.0139 0.0860Valb1561 0.0005 STARI 0.0044 STARI M06A- STARI 0.0000 0.1788 0223150.9995 0.8168 Valb1328 0.6469 EL 0.5180 EL M06A- STARI 0.0097 0.0960120214 0.3434 0.3860 Valb0329 0.2186 STARI 0.2140 STARI M09A- STARI0.0048 0.0740 022315 0.7767 0.7120 Valb0070 0.0212 STARI 0.2480 STARIM09A- STARI 0.0066 0.0980 120214 0.9722 0.6540 Valb1052 0.0298 STARI0.3840 STARI M13A- STARI 0.0061 0.1920 022315 0.9640 0.4240 Valb08090.0020 EL 0.1560 EL M13A- STARI 0.9494 0.6200 120214 0.0486 0.2240 BValb1256 0.0016 STARI 0.2340 STARI M16A- STARI 0.0002 0.1440 0223150.9982 0.6220 Valb1100 0.0232 STARI 0.2400 STARI M16A- STARI 0.00550.0820 120214 0.9713 0.6780 Valb1236 0.1166 STARI 0.4740 EL M19A- STARI0.0227 0.2340 022315 0.8607 0.2920 Valb0580 0.1942 STARI 0.4080 STARIM19A- STARI 0.1003 0.1800 120214 0.7055 0.4120 Valb1525 0.9962 EL 0.3660STARI M22A- STARI 0.0000 0.1700 022315 0.0038 0.4640 Valb0534 0.1791STARI 0.3520 STARI M22A- STARI 0.0000 0.1880 120214 0.8208 0.4600Valb0556 0.3684 STARI 0.3120 STARI M26A- STARI 0.1161 0.0300 0223150.5155 0.6580 Valb0116 0.4121 STARI 0.1900 STARI M26A- STARI 0.00050.0560 120214 0.5874 0.7540 Valb0461 0.0048 STARI 0.2000 STARI M27A-STARI 0.0293 0.0860 022315 0.9659 0.7140 Valb0266 0.0169 STARI 0.1300STARI M27A- STARI 0.0001 0.0560 120214 0.9830 0.8140 Valb0447 0.0016STARI 0.1280 STARI S03- STARI 0.1106 0.0780 022315 0.8877 0.7940Valb0026 0.0005 STARI 0.1320 STARI S03- STARI 0.0004 0.0640 1202140.9992 0.8040 Valb1114 0.0013 STARI 0.1800 STARI S09- STARI 0.00040.2660 022315 0.9982 0.5540 Valb0464 0.1404 STARI 0.1320 STARI S09-STARI 0.0000 0.2000 120214 0.8596 0.6680 Valb1292 0.0002 STARI 0.1360STARI S17- STARI 0.0000 0.1980 022315 0.9997 0.6660 Valb0754 0.0001STARI 0.0980 STARI S17- STARI 0.0000 0.1480 120214 0.9999 0.7540Valb0434 0.0209 STARI 0.1780 STARI S21- STARI 0.0896 0.2000 0223150.8896 0.6220 Valb0044 0.0148 STARI 0.2560 STARI S21- STARI 0.02030.1920 120214 0.9649 0.5520 Valb0873 0.0079 STARI 0.1340 STARI S33-STARI 0.0169 0.2480 022315 0.9753 0.6180 Valb0352 0.0003 STARI 0.1280STARI S33- STARI 0.0087 0.2180 120214 0.9910 0.6540 Valb1141 0.0000STARI 0.1060 STARI S39- STARI 0.0169 0.1100 022315 0.9831 0.7840Valb0480 0.0000 STARI 0.0540 STARI S39- STARI 0.0002 0.0500 1202140.9998 0.8960 Valb0618 0.0015 STARI 0.2640 STARI S43- STARI 0.00100.2060 022315 0.9975 0.5300 Valb0660 0.0018 STARI 0.2700 STARI S43-STARI 0.0008 0.1400 120214 0.9973 0.5900 Valb0223 0.0002 STARI 0.1080STARI S47- STARI 0.0340 0.3080 022315 0.9658 0.5840 Valb0054 0.0023STARI 0.0640 STARI S47- STARI 0.0168 0.0740 120214 0.9808 0.8620Valb0335 0.0085 STARI 0.0660 STARI S53- STARI 0.0023 0.0440 0223150.9893 0.8900 Valb0197 0.0050 STARI 0.0320 STARI S53- STARI 0.00010.0320 120214 0.9949 0.9360 Valb0409 0.0714 STARI 0.1680 STARI S55-STARI 0.0715 0.1420 022315 0.8571 0.6900 Valb0060 0.0119 STARI 0.1020STARI S55- STARI 0.0059 0.1180 120214 0.9821 0.7800 Valb0437 0.0001STARI 0.0800 STARI S65- STARI 0.0078 0.1060 022315 0.9921 0.8140Valb0093 0.0000 STARI 0.1060 STARI S65- STARI 0.0001 0.0720 1202140.9999 0.8220

What is claimed is:
 1. A method for treating a subject with Lymedisease, the method comprising: (a) obtaining a disease score from amass spectrometry based test; (b) diagnosing the subject with Lymedisease based on the disease score; and (c) administering a treatment tothe subject diagnosed with Lyme disease based on the disease score,wherein the treatment is a pharmacological treatment for Lyme diseaseselected from an antibiotic, an antibacterial agent, an immunemodulator, an anti-inflammatory agent, or a combination thereof; whereinthe mass spectrometry based test comprises: (i) deproteinizing a bloodsample from a subject to produce a metabolite extract; (ii) performingliquid chromatography coupled to mass spectrometry on a sample of themetabolite extract; (iii) providing abundance values for each molecularfeature in Table A, Table C, or Table D: TABLE A m/z Retention MF(positive Time (see # Name ion) Mass examples) 1 CSU/CDC-001 166.0852165.078 1.86 2 CSU/CDC-012 270.3156 269.3076 18.02 3 CSU/CDC-013284.3314 283.3236 18.13 4 CSU/CDC-014 300.6407 599.268 18.27 5CSU/CDC-019 300.2892 299.2821 19.66 6 CSU/CDC-039 734.5079 1449.975317.81 7 CSU/CDC-062 370.1837 369.1757 19.7 8 CSU/CDC-066 811.1942810.1869 12.07 9 CSU/CDC-067 947.7976 946.7936 14.55 10 CSU/CDC-072410.2033 409.196 17.18 11 CSU/CDC-075 1487.0005 1485.9987 18.17 12CSU/CDC-086 137.0463 136.0378 1.37 13 CSU/CDC-107 811.7965 810.788212.07 14 CSU/CDC-132 616.1776 615.1699 15.43 15 CSU/CDC-152 713.4492712.4391 19.35 16 CSU/CDC-155 502.3376 484.3039 19.87 17 CSU/CDC-158415.3045 414.2978 20.19 18 CSU/CDC-164 366.3729 365.3655 22.79 19CSU/CDC-166 333.1446 332.1373 12.89 20 CSU/CDC-205 241.1069 240.099614.7 21 CSU/CDC-211 464.1916 463.1849 13.05 22 CSU/CDC-212 1249.20451248.1993 15.31 23 CSU/CDC-213 1248.9178 1247.9141 15.3 24 CSU/CDC-219158.1539 157.1466 15.36 25 CSU/CDC-227 529.3381 528.3296 16.89 26CSU/CDC-229 282.2776 264.2456 20.56 27 CSU/CDC-235 190.1260 189.118714.12 28 CSU/CDC-238 382.3675 381.3603 20.23 29 CSU/CDC-244 477.2968476.2898 22.79 30 CSU/CDC-248 459.3968 458.3904 19.08 31 CSU/CDC-253342.2635 341.2565 15.62 32 CSU/CDC-254 529.3827 1022.6938 17.86 33CSU/CDC-258 459.2502 458.2429 19.02 34 CSU/CDC-002 239.0919 238.084411.66 35 CSU/CDC-028 389.2174 388.2094 15.47 36 CSU/CDC-182 285.2065284.1991 16.02 37 CSU/CDC-204 279.1693 278.1629 11.05 38 CSU/CDC-247714.6967 1427.3824 11.76 Predicted Chemical Structure Compound (based onMetabolite MF Predicted accurate Class or # Name Formula mass) Pathway 1CSU/CDC-001 C₉H₁₁NO₂ Phenyl- Phenyl alanine alanine metabolism 2CSU/CDC-012 C₁₈H₃₉N — — 3 CSU/CDC-013 C₁₉ H₄₁N — — 4 CSU/CDC-014C₃₃H₃₇N₅O₆ Asp Phe Peptide Arg Tyr (SEQ ID NO: 1) 5 CSU/CDC-019C₁₈H₃₇NO₂ Palmitoyl N-acyl eth- ethanola- anolamine mide metabolism 6CSU/CDC-039 — — — 7 CSU/CDC-062 C₁₉H₂₃N₅O₃ — — 8 CSU/CDC-066 C₄₂H₃₀N₆O₁₂— — 9 CSU/CDC-067 C₆₂H₁₀₆O₆ TAG Triacyl- (59:7) glycerol metabolism 10CSU/CDC-072 — — — 11 CSU/CDC-075 — — — 12 CSU/CDC-086 C₄H₈O₅ ThreonateSugar metabolite 13 CSU/CDC-107 — — — 14 CSU/CDC-132 — — — 15CSU/CDC-152 C₃₈H₆₅O₁₀P PG(32:5) Glycero- phospho- lipid metabolism 16CSU/CDC-155 C₂₇H₄₀N₄O₄ Gln Leu Peptide Pro Lys (SEQ ID NO: 2) 17CSU/CDC-158 — — — 18 CSU/CDC-164 — — — 19 CSU/CDC-166 C₁₂H₂₀N₄O₇ Glu GlnPeptide Gly 20 CSU/CDC-205 C₁₂H₁₆O₅ 3-Car- Fatty acid boxy-4- methyl-5-metabolism propyl-2- furanpro- panoic acid (CMPF) 21 CSU/CDC-211C₁₆H₂₉N₇O₇S Arg Asp Peptide Cys Ala (SEQ ID NO: 3) 22 CSU/CDC-212 — — —23 CSU/CDC-213 — — — 24 CSU/CDC-219 — — — 25 CSU/CDC-227 C₂₄H₄₄N₆O₇ GlnVal Peptide Leu Leu Gly (SEQ ID NO: 4) 26 CSU/CDC-229 C₁₈H₃₂O — — 27CSU/CDC-235 C₉H₁₉NOS 8-Methyl- 2-oxocar- thiooctanal boxylic doxime acidmetabolism 28 CSU/CDC-238 C₂₄H₄₇NO₂ Erucicoyl N-acyl ethanol- ethanol-amide amine metabolism 29 CSU/CDC-244 C₃₁ H₄₀O₄ Lys Lys Peptide Thr Thr(SEQ ID NO: 5) 30 CSU/CDC-248 — — — 31 CSU/CDC-253 C₁₉H₃₅NO₄ — — 32CSU/CDC-254 — — — 33 CSU/CDC-258 C₂₃H₃₉O₇P Lyso Glycero- PA(20:4)hospholipid metabolism 34 CSU/CDC-002 C₁₂H₁₄O₅ Trans- Phenyl- 2,3,4-tri-propanoid methoxy- and cinnamate polyketide metabolism 35 CSU/CDC-028C₁₉H₃₂O₈ Methyl 10, Fatty acid 12,13,15- metabolism bisepidi- oxy-16-hydro- peroxy- 8E-octade- cenoate 36 CSU/CDC-182 C₁₆H₂₈O₄ — — 37CSU/CDC-204 C₁₅H₂₂N₂O₃ Phe Leu Dipeptide 38 CSU/CDC-247 — — —

TABLE C m/z Retention MF (positive Time (see # Name ion) Mass examples)1 CSU/CDC-001 166.0852 165.078 1.86 2 CSU/CDC-012 270.3156 269.307618.02 3 CSU/CDC-013 284.3314 283.3236 18.13 4 CSU/CDC-014 300.6407599.268 18.27 5 CSU/CDC-019 300.2892 299.2821 19.66 6 CSU/CDC-039734.5079 1449.9753 17.81 7 CSU/CDC-062 370.1837 369.1757 19.7 8CSU/CDC-066 811.1942 810.1869 12.07 9 CSU/CDC-067 947.7976 946.793614.55 10 CSU/CDC-072 410.2033 409.196 17.18 11 CSU/CDC-075 1487.00051485.9987 18.17 12 CSU/CDC-086 137.0463 136.0378 1.37 13 CSU/CDC-107811.7965 810.7882 12.07 14 CSU/CDC-132 616.1776 615.1699 15.43 15CSU/CDC-152 713.4492 712.4391 19.35 16 CSU/CDC-155 502.3376 484.303919.87 17 CSU/CDC-158 415.3045 414.2978 20.19 18 CSU/CDC-164 366.3729365.3655 22.79 19 CSU/CDC-166 333.1446 332.1373 12.89 20 CSU/CDC-205241.1069 240.0996 14.7 21 CSU/CDC-211 464.1916 463.1849 13.05 22CSU/CDC-212 1249.2045 1248.1993 15.31 23 CSU/CDC-213 1248.9178 1247.914115.3 24 CSU/CDC-219 158.1539 157.1466 15.36 25 CSU/CDC-227 529.3381528.3296 16.89 26 CSU/CDC-229 282.2776 264.2456 20.56 27 CSU/CDC-235190.1260 189.1187 14.12 28 CSU/CDC-238 382.3675 381.3603 20.23 29CSU/CDC-244 477.2968 476.2898 22.79 30 CSU/CDC-248 459.3968 458.390419.08 31 CSU/CDC-253 342.2635 341.2565 15.62 32 CSU/CDC-254 529.38271022.6938 17.86 33 CSU/CDC-258 459.2502 458.2429 19.02 34 CSU/CDC-003886.4296 1770.8438 12.18 35 CSU/CDC-004 181.0859 180.0788 14.7 36CSU/CDC-006 286.1444 285.1371 16.08 37 CSU/CDC-008 463.2339 462.224816.36 38 CSU/CDC-009 242.2844 241.2772 17.1 39 CSU/CDC-017 590.4237589.4194 19.24 40 CSU/CDC-026 553.3904 552.3819 23.38 41 CSU/CDC-030399.2364 398.2313 16.23 42 CSU/CDC-042 580.4144 1158.8173 18.26 43CSU/CDC-052 704.4985 1372.925 18.7 44 CSU/CDC-061 623.4521 1210.836219.55 45 CSU/CDC-070 389.2178 388.2099 15.52 46 CSU/CDC-074 1111.66901110.6656 17.89 47 CSU/CDC-083 482.4040 481.3976 19.99 48 CSU/CDC-084533.1929 532.1854 20.84 49 CSU/CDC-087 466.3152 465.3085 14.73 50CSU/CDC-091 683.4728 1347.9062 17.56 51 CSU/CDC-095 227.0897 204.10029.68 52 CSU/CDC-098 183.1016 182.0943 10.89 53 CSU/CDC-099 476.3055475.2993 11.09 54 CSU/CDC-112 215.1283 214.1209 12.32 55 CSU/CDC-115519.1881 518.1813 12.33 56 CSU/CDC-128 1086.1800 2170.3435 15.38 57CSU/CDC-133 285.2061 284.1993 15.99 58 CSU/CDC-134 357.1363 356.128415.98 59 CSU/CDC-136 299.1853 298.1781 16.24 60 CSU/CDC-137 334.2580333.2514 16.36 61 CSU/CDC-138 317.2317 316.2254 16.63 62 CSU/CDC-141331.2471 330.2403 17.26 63 CSU/CDC-144 583.3480 582.3379 18.04 64CSU/CDC-157 648.4672 647.4609 19.98 65 CSU/CDC-165 445.2880 854.508712.48 66 CSU/CDC-181 1486.7386 2971.4668 14.97 67 CSU/CDC-183 668.46861317.8969 18.04 68 CSU/CDC-184 454.2924 436.2587 18.1 69 CSU/CDC-186607.9324 606.9246 19.01 70 CSU/CDC-188 521.4202 503.3858 21.06 71CSU/CDC-193 176.0746 175.0667 2.31 72 CSU/CDC-194 596.9082 1191.803319.1 73 CSU/CDC-203 532.5606 531.5555 18.38 74 CSU/CDC-206 337.1667336.1599 20.67 75 CSU/CDC-210 415.1634 207.0784 12.2 76 CSU/CDC-218364.3407 346.3068 20.72 77 CSU/CDC-222 989.5004 1976.9858 12.03 78CSU/CDC-224 819.6064 1635.8239 12.06 79 CSU/CDC-237 286.2737 285.266619.08 80 CSU/CDC-245 614.4833 613.4772 19.78 81 CSU/CDC-250 298.2740297.2668 16.44 82 CSU/CDC-252 1003.7020 1002.696 18.46 PredictedChemical Structure Compound (based on Metabolite Predicted accurateClass or MF # Name Formula mass) Pathway 1 CSU/CDC-001 C₉H₁₁NO₂Phenylal- Phenylalanine anine metabolism 2 CSU/CDC-012 C₁₈H₃₉N — — 3CSU/CDC-013 C₁₉ H₄₁ N — — 4 CSU/CDC-014 C₃₃H₃₇N₅O₆ Asp Phe Peptide ArgTyr (SEQ ID NO: 1) 5 CSU/CDC-019 C₁₈H₃₇NO₂ Palmitoyl N-acyl ethanol-ethanolamine amide metabolism 6 CSU/CDC-039 — — — 7 CSU/CDC-062C₁₉H₂₃N₅O₃ — — 8 CSU/CDC-066 C₄₂H₃₀N₆O₁₂ — — 9 CSU/CDC-067 C₆₂H₁₀₆O₆TAG(59:7) Triacylglycerol metabolism 10 CSU/CDC-072 — — — 11 CSU/CDC-075— — — 12 CSU/CDC-086 C₄H₈O₅ Threonate Sugar metabolite 13 CSU/CDC-107 —— — 14 CSU/CDC-132 — — — 15 CSU/CDC-152 C₃₈H₆₅O₁₀P PG(32:5) Glycerophos-pholipid metabolism 16 CSU/CDC-155 C₂₇H₄₀N₄O₄ Gln Leu Peptide Pro Lys(SEQ ID NO: 2) 17 CSU/CDC-158 — — — 18 CSU/CDC-164 — — — 19 CSU/CDC-166C₁₂H₂₀N₄O₇ Glu Gln Gly Peptide 20 CSU/CDC-205 C₁₂H₁₆O₅ 3-Carboxy- Fattyacid 4-methyl-5- metabolism propyl-2- furanpro- panoic acid (CMPF) 21CSU/CDC-211 C₁₆H₂₉N₇O₇S Arg Asp Peptide Cys Ala (SEQ ID NO: 3) 22CSU/CDC-212 — — — 23 CSU/CDC-213 — — — 24 CSU/CDC-219 — — — 25CSU/CDC-227 C₂₄H₄₄N₆O₇ Gln Val Peptide Leu Leu Gly (SEQ ID NO: 4) 26CSU/CDC-229 C₁₈H₃₂O — — 27 CSU/CDC-235 C₉H₁₉NOS 8-Methyl-2-oxocarboxylic thiooctanal acid metabolism doxime 28 CSU/CDC-238C₂₄H₄₇NO₂ Erucicoyl N-acyl ethanolamine ethanolamide metabolism 29CSU/CDC-244 C₃₁ H₄₀O₄ Lys Lys Peptide Thr Thr (SEQ ID NO: 5) 30CSU/CDC-248 — — — 31 CSU/CDC-253 C₁₉H₃₅NO₄ — — 32 CSU/CDC-254 — — — 33CSU/CDC-258 C₂₃H₃₉O₇P Lyso Glycerohos- PA(20:4) pholipid metabolism 34CSU/CDC-003 — — — 35 CSU/CDC-004 C₁₀H₁₂O₃ 5′-(3′- Endogenous Methoxy-4′-metabolite hydroxy- associated phenyl)- with gamma- microbiomevalerolactone 36 CSU/CDC-006 C₁₇H₁₉NO₃ Piperine Alkaloid metabolism 37CSU/CDC-008 C₂₅H₃₄O₈ Ala Lys Peptide Met Asn (SEQ ID NO: 6) 38CSU/CDC-009 C₁₆H₃₅N — — 39 CSU/CDC-017 — — — 40 CSU/CDC-026 C₃₅H₅₂O₅Furohyper- Endogenous forin metabolite - derived from food 41CSU/CDC-030 — — — 42 CSU/CDC-042 — — — 43 CSU/CDC-052 — — — 44CSU/CDC-061 — — — 45 CSU/CDC-070 C₁₉H₃₂O₈ — — 46 CSU/CDC-074 — — — 47CSU/CDC-083 — — — 48 CSU/CDC-084 C₂₃H₂₈N₆O₉ Asp His Peptide Phe Asp (SEQID NO: 7) 49 CSU/CDC-087 C₂₆H₄₃NO₆ Glycocholic Bile acid acid metabolism50 CSU/CDC-091 — — — 51 CSU/CDC-095 C₉H₁₆O₅ — — 52 CSU/CDC-098 C₁₀H₁₄O₃— — 53 CSU/CDC-099 C₂₆H₄₁N₃O₅ — — 54 CSU/CDC-112 C₁₁H₁₈O₄ alpha-Endogenous Carboxy- delta- metabolite - decalactone derived from food 55CSU/CDC-115 C₂₀H₃₀N₄O₁₂ Poly-g-D- Poly D-glutamate glutamate metabolism56 CSU/CDC-128 — — — 57 CSU/CDC-133 C₁₆H₂₈O₄ — — 58 CSU/CDC-134 C₂₀H₂₀O₆Xanthoxylol Endogenous metabolite - derived from food 59 CSU/CDC-136C₁₆H₂₆O₅ Tetranor- Prostaglandin PGE1 metabolism 60 CSU/CDC-137 — — — 61CSU/CDC-138 — — — 62 CSU/CDC-141 C₁₈H₃₄O₅ 11,12,13- Fatty acidtrihydroxy-9- metabolism octadecenoic acid 63 CSU/CDC-144 C₂₇H₄₆N₆O₈ LeuLys Glu Peptide Pro Pro (SEQ ID NO: 8) 64 CSU/CDC-157 C₃₄H₆₆NO₈PPE(29:1) Glycerophos- pholipid metabolism 65 CSU/CDC-165 C₄₅H₇₄O₁₅(3b,21b)-12- Endogenous Oleanene- metabolite - 3,21,28- derived triol28- from food [arabinosyl- (1->3)- arabinosyl- (1->3)- arabinoside] 66CSU/CDC-181 — — — 67 CSU/CDC-183 C₁₆H₂₈O₄ Omphalotin Endogenous Ametabolite - derived from food 68 CSU/CDC-184 C₂₁H₄₁O₇P Lyso-Glycerophos- PA(18:1) pholipid metabolism 69 CSU/CDC-186 — — — 70CSU/CDC-188 — — — 71 CSU/CDC-193 — — — 72 CSU/CDC-194 — — — 73CSU/CDC-203 — — — 74 CSU/CDC-206 C₁₂H₂₄N₄O₇ — — 75 CSU/CDC-210 C₈H₉N₅O₂6-Amino- Endogenous 9H-purine- metabolite - 9-propanoic derived acidfrom food 76 CSU/CDC-218 — — — 77 CSU/CDC-222 — — — 78 CSU/CDC-224 — — —79 CSU/CDC-237 C₁₇H₃₅NO₂ Pentade- N-acyl canoyl ethanolamineethanolamide metabolism 80 CSU/CDC-245 — — — 81 CSU/CDC-250 C₁₈H₃₅NO₂3-Ketospin- Sphingolipid gosine metabolism 82 CSU/CDC-252 — — —

TABLE D m/z Retention (positive Time (see MF # Name ion) Mass examples)1 CSU/CDC-001 166.0852 165.078 1.86 2 CSU/CDC-012 270.3156 269.307618.02 3 CSU/CDC-013 284.3314 283.3236 18.13 4 CSU/CDC-014 300.6407599.268 18.27 5 CSU/CDC-019 300.2892 299.2821 19.66 6 CSU/CDC-039734.5079 1449.9753 17.81 7 CSU/CDC-062 370.1837 369.1757 19.7 8CSU/CDC-066 811.1942 810.1869 12.07 9 CSU/CDC-067 947.7976 946.793614.55 10 CSU/CDC-072 410.2033 409.196 17.18 11 CSU/CDC-075 1487.00051485.9987 18.17 12 CSU/CDC-086 137.0463 136.0378 1.37 13 CSU/CDC-107811.7965 810.7882 12.07 14 CSU/CDC-132 616.1776 615.1699 15.43 15CSU/CDC-152 713.4492 712.4391 19.35 16 CSU/CDC-155 502.3376 484.303919.87 17 CSU/CDC-158 415.3045 414.2978 20.19 18 CSU/CDC-164 366.3729365.3655 22.79 19 CSU/CDC-166 333.1446 332.1373 12.89 20 CSU/CDC-205241.1069 240.0996 14.7 21 CSU/CDC-211 464.1916 463.1849 13.05 22CSU/CDC-212 1249.2045 1248.1993 15.31 23 CSU/CDC-213 1248.9178 1247.914115.3 24 CSU/CDC-219 158.1539 157.1466 15.36 25 CSU/CDC-227 529.3381528.3296 16.89 26 CSU/CDC-229 282.2776 264.2456 20.56 27 CSU/CDC-235190.1260 189.1187 14.12 28 CSU/CDC-238 382.3675 381.3603 20.23 29CSU/CDC-244 477.2968 476.2898 22.79 30 CSU/CDC-248 459.3968 458.390419.08 31 CSU/CDC-253 342.2635 341.2565 15.62 32 CSU/CDC-254 529.38271022.6938 17.86 33 CSU/CDC-258 459.2502 458.2429 19.02 34 CSU/CDC-002239.0919 238.0844 11.66 35 CSU/CDC-028 389.2174 388.2094 15.47 36CSU/CDC-182 285.2065 284.1991 16.02 37 CSU/CDC-204 279.1693 278.162911.05 38 CSU/CDC-247 714.6967 1427.3824 11.76 39 CSU/CDC-003 886.42961770.8438 12.18 40 CSU/CDC-004 181.0859 180.0788 14.7 41 CSU/CDC-006286.1444 285.1371 16.08 42 CSU/CDC-008 463.2339 462.2248 16.36 43CSU/CDC-009 242.2844 241.2772 17.1 44 CSU/CDC-017 590.4237 589.419419.24 45 CSU/CDC-026 553.3904 552.3819 23.38 46 CSU/CDC-030 399.2364398.2313 16.23 47 CSU/CDC-042 580.4144 1158.8173 18.26 48 CSU/CDC-052704.4985 1372.925 18.7 49 CSU/CDC-061 623.4521 1210.8362 19.55 50CSU/CDC-070 389.2178 388.2099 15.52 51 CSU/CDC-074 1111.6690 1110.665617.89 52 CSU/CDC-083 482.4040 481.3976 19.99 53 CSU/CDC-084 533.1929532.1854 20.84 54 CSU/CDC-087 466.3152 465.3085 14.73 55 CSU/CDC-091683.4728 1347.9062 17.56 56 CSU/CDC-095 227.0897 204.1002 9.68 57CSU/CDC-098 183.1016 182.0943 10.89 58 CSU/CDC-099 476.3055 475.299311.09 59 CSU/CDC-112 215.1283 214.1209 12.32 60 CSU/CDC-115 519.1881518.1813 12.33 61 CSU/CDC-128 1086.1800 2170.3435 15.38 62 CSU/CDC-133285.2061 284.1993 15.99 63 CSU/CDC-134 357.1363 356.1284 15.98 64CSU/CDC-136 299.1853 298.1781 16.24 65 CSU/CDC-137 334.2580 333.251416.36 66 CSU/CDC-138 317.2317 316.2254 16.63 67 CSU/CDC-141 331.2471330.2403 17.26 68 CSU/CDC-144 583.3480 582.3379 18.04 69 CSU/CDC-157648.4672 647.4609 19.98 70 CSU/CDC-165 445.2880 854.5087 12.48 71CSU/CDC-181 1486.7386 2971.4668 14.97 72 CSU/CDC-183 668.4686 1317.896918.04 73 CSU/CDC-184 454.2924 436.2587 18.1 74 CSU/CDC-186 607.9324606.9246 19.01 75 CSU/CDC-188 521.4202 503.3858 21.06 76 CSU/CDC-193176.0746 175.0667 2.31 77 CSU/CDC-194 596.9082 1191.8033 19.1 78CSU/CDC-203 532.5606 531.5555 18.38 79 CSU/CDC-206 337.1667 336.159920.67 80 CSU/CDC-210 415.1634 207.0784 12.2 81 CSU/CDC-218 364.3407346.3068 20.72 82 CSU/CDC-222 989.5004 1976.9858 12.03 83 CSU/CDC-224819.6064 1635.8239 12.06 84 CSU/CDC-237 286.2737 285.2666 19.08 85CSU/CDC-245 614.4833 613.4772 19.78 86 CSU/CDC-250 298.2740 297.266816.44 87 CSU/CDC-252 1003.7020 1002.696 18.46 88 CSU/CDC-005 223.0968222.0895 14.69 89 CSU/CDC-007 286.1437 285.1364 16.06 90 CSU/CDC-0101112.6727 1111.6663 17.86 91 CSU/CDC-011 454.2923 453.2867 18.08 92CSU/CDC-015 522.3580 521.3483 18.5 93 CSU/CDC-016 363.2192 362.213218.58 94 CSU/CDC-018 388.3939 387.3868 19.53 95 CSU/CDC-020 256.2632255.2561 20.08 96 CSU/CDC-021 394.3515 376.3171 20.09 97 CSU/CDC-022228.1955 227.1885 20.99 98 CSU/CDC-023 284.2943 283.2872 21.15 99CSU/CDC-024 338.3430 337.3344 22.14 100 CSU/CDC-025 689.5604 688.550422.52 101 CSU/CDC-027 432.2803 431.2727 10.8 102 CSU/CDC-029 385.2211384.2147 15.84 103 CSU/CDC-031 449.3261 879.6122 17.07 104 CSU/CDC-032467.3821 444.2717 17.1 105 CSU/CDC-033 836.5936 835.5845 17.15 106CSU/CDC-034 792.5646 791.5581 17.17 107 CSU/CDC-035 356.2802 355.272217.35 108 CSU/CDC-036 806.5798 805.5746 17.71 109 CSU/CDC-037 762.5582761.5482 17.79 110 CSU/CDC-038 718.5308 700.4946 17.88 111 CSU/CDC-040690.4825 1361.924 17.95 112 CSU/CDC-041 426.1798 425.1725 18.03 113CSU/CDC-043 741.5154 1481.0142 18.24 114 CSU/CDC-044 864.6245 863.616618.17 115 CSU/CDC-045 558.4017 1080.7347 18.28 116 CSU/CDC-046 719.50121402.9377 18.26 117 CSU/CDC-047 536.3897 1053.7382 18.36 118 CSU/CDC-048538.8674 1058.696 18.4 119 CSU/CDC-049 653.4619 1270.8593 18.43 120CSU/CDC-050 732.5450 714.5092 18.47 121 CSU/CDC-051 748.5232 1478.005918.58 122 CSU/CDC-053 682.4841 1328.9008 18.77 123 CSU/CDC-054 360.3615359.3555 18.89 124 CSU/CDC-055 441.2412 440.2325 19.09 125 CSU/CDC-056638.4554 1240.847 18.92 126 CSU/CDC-057 755.5311 1474.9941 18.94 127CSU/CDC-058 711.5023 1386.9417 19.09 128 CSU/CDC-059 784.5530 1567.090819.27 129 CSU/CDC-060 645.4660 1271.8896 19.36 130 CSU/CDC-063 300.2886282.2569 19.84 131 CSU/CDC-064 309.0981 308.0913 2.06 132 CSU/CDC-065561.2965 1120.5778 11.7 133 CSU/CDC-068 1106.2625 2209.5193 14.53 134CSU/CDC-069 371.2070 370.1997 15.52 135 CSU/CDC-071 443.2649 442.25615.52 136 CSU/CDC-073 850.6093 849.6009 17.63 137 CSU/CDC-076 697.48961358.909 18.32 138 CSU/CDC-077 439.8234 877.6325 18.71 139 CSU/CDC-078567.8897 566.8818 18.73 140 CSU/CDC-079 435.2506 434.243 19 141CSU/CDC-080 834.6136 833.6057 18.83 142 CSU/CDC-081 534.8834 533.877118.82 143 CSU/CDC-082 468.8441 467.8373 19.13 144 CSU/CDC-085 312.3259311.319 22.05 145 CSU/CDC-088 228.1955 227.1884 15.22 146 CSU/CDC-089385.2211 384.2143 15.83 147 CSU/CDC-090 403.2338 402.2253 15.84 148CSU/CDC-092 675.4753 1348.9377 18.37 149 CSU/CDC-093 682.4841 1345.925718.76 150 CSU/CDC-094 762.5401 1506.0367 19.36 151 CSU/CDC-177 189.1122188.1049 12.27 152 CSU/CDC-097 169.0860 168.0786 9.94 153 CSU/CDC-100276.1263 275.1196 11.16 154 CSU/CDC-101 314.0672 313.06 11.56 155CSU/CDC-102 201.1122 200.1047 11.56 156 CSU/CDC-103 115.0391 114.031811.57 157 CSU/CDC-104 491.1569 490.1504 11.56 158 CSU/CDC-105 241.1054218.1157 11.57 159 CSU/CDC-106 105.0914 104.0841 11.57 160 CSU/CDC-108311.1472 328.1391 12.22 161 CSU/CDC-109 271.1543 270.1464 12.24 162CSU/CDC-110 169.0860 168.0787 12.24 163 CSU/CDC-111 187.0967 186.088912.24 164 CSU/CDC-113 475.1635 474.1547 12.25 165 CSU/CDC-114 129.0547128.0474 12.33 166 CSU/CDC-116 125.0599 124.0527 13.12 167 CSU/CDC-117247.1550 246.1469 13.13 168 CSU/CDC-118 517.2614 516.2544 13.13 169CSU/CDC-119 301.0739 300.0658 13.14 170 CSU/CDC-120 327.1773 304.188514.17 171 CSU/CDC-121 387.2023 386.1935 14.51 172 CSU/CDC-122 875.84511749.684 14.55 173 CSU/CDC-123 737.5118 736.5056 14.52 174 CSU/CDC-1241274.3497 1273.3481 14.96 175 CSU/CDC-125 1274.2092 1273.2 14.96 176CSU/CDC-126 1486.5728 2971.1328 14.95 177 CSU/CDC-127 965.3818 964.372715.37 178 CSU/CDC-129 1086.0562 2170.0908 15.38 179 CSU/CDC-1301086.4344 2169.8474 15.39 180 CSU/CDC-131 1240.7800 1239.7712 15.38 181CSU/CDC-135 317.1956 316.1885 16.24 182 CSU/CDC-139 299.2219 298.214816.64 183 CSU/CDC-140 748.5408 747.5317 17.23 184 CSU/CDC-142 712.49351422.9749 17.82 185 CSU/CDC-143 674.5013 673.4957 17.99 186 CSU/CDC-145677.9537 676.9478 18.36 187 CSU/CDC-146 531.3522 530.3457 18.4 188CSU/CDC-147 585.2733 584.2649 18.39 189 CSU/CDC-148 513.3431 512.335218.4 190 CSU/CDC-149 611.9156 610.9073 18.59 191 CSU/CDC-150 549.0538531.0181 18.38 192 CSU/CDC-151 755.5311 1509.0457 18.93 193 CSU/CDC-153599.4146 598.4079 19.59 194 CSU/CDC-154 762.5029 761.4919 19.66 195CSU/CDC-156 741.4805 740.4698 19.96 196 CSU/CDC-159 516.3532 498.319920.27 197 CSU/CDC-160 769.5099 768.5018 20.53 198 CSU/CDC-161 862.5881861.5818 20.86 199 CSU/CDC-162 837.5358 836.5274 21.11 200 CSU/CDC-163558.3995 540.367 21.44 201 CSU/CDC-167 1105.9305 2209.8462 14.53 202CSU/CDC-168 329.1049 328.0976 14.61 203 CSU/CDC-169 1241.2053 1240.215.38 204 CSU/CDC-170 1088.6731 1087.6676 17.85 205 CSU/CDC-171 667.4391666.4323 20.35 206 CSU/CDC-172 133.0497 132.0423 11.57 207 CSU/CDC-173259.1540 258.1469 11.75 208 CSU/CDC-174 311.1472 288.1574 12.23 209CSU/CDC-175 147.0652 146.0579 12.33 210 CSU/CDC-176 169.0860 168.078812.29 211 CSU/CDC-096 187.0965 186.0894 9.93 212 CSU/CDC-178 139.1116138.1044 12.95 213 CSU/CDC-179 515.2811 514.2745 13.14 214 CSU/CDC-180283.1522 282.1444 13.93 215 CSU/CDC-185 706.9750 705.9684 18.7 216CSU/CDC-187 834.5575 833.5502 20.32 217 CSU/CDC-189 683.4727 1364.929417.54 218 CSU/CDC-190 728.9890 1455.9633 18.63 219 CSU/CDC-191 726.51041451.0035 18.64 220 CSU/CDC-192 633.9280 632.9206 18.47 221 CSU/CDC-195209.0784 208.0713 9.92 222 CSU/CDC-196 792.5483 1566.055 18.46 223CSU/CDC-197 618.9221 1218.8083 19.02 224 CSU/CDC-198 549.0543 531.018918.37 225 CSU/CDC-199 553.7262 552.7188 18.74 226 CSU/CDC-200 756.0320755.0266 18.95 227 CSU/CDC-201 639.6307 638.6205 19.58 228 CSU/CDC-202753.4414 730.4513 19.37 229 CSU/CDC-207 328.3204 327.3148 20.72 230CSU/CDC-208 514.3718 1009.7122 18.42 231 CSU/CDC-209 630.4594 1241.873719.95 232 CSU/CDC-214 244.2270 243.22 17.17 233 CSU/CDC-215 463.3426924.6699 18.08 234 CSU/CDC-216 468.3892 450.3553 19.17 235 CSU/CDC-217438.3787 420.3453 19.93 236 CSU/CDC-220 792.0006 790.995 12.04 237CSU/CDC-221 792.2025 791.1947 12.04 238 CSU/CDC-223 791.6016 790.59412.04 239 CSU/CDC-225 1115.5593 2228.1028 14.95 240 CSU/CDC-2261486.9176 2970.7976 14.96 241 CSU/CDC-228 430.3161 412.2845 20.23 242CSU/CDC-230 297.2793 296.2734 20.66 243 CSU/CDC-231 714.3655 1426.71811.73 244 CSU/CDC-232 714.5306 1427.0479 11.76 245 CSU/CDC-233 989.74991977.4865 12.03 246 CSU/CDC-234 221.0744 220.0672 13.7 247 CSU/CDC-236313.2734 312.2663 18.91 248 CSU/CDC-239 337.2712 314.282 20.66 249CSU/CDC-240 441.3687 440.3614 21.26 250 CSU/CDC-241 425.3735 424.366621.5 251 CSU/CDC-242 356.3517 355.3448 21.67 252 CSU/CDC-243 393.2970370.3082 22.46 253 CSU/CDC-246 167.9935 166.9861 13.2 254 CSU/CDC-249677.6170 676.6095 20.71 255 CSU/CDC-251 460.2695 459.2627 16.87 256CSU/CDC-255 630.4765 612.4417 18.11 257 CSU/CDC-256 514.3734 1026.728118.41 258 CSU/CDC-257 667.4754 1315.916 19.28 259 CSU/CDC-259 516.85491031.6945 18.43 260 CSU/CDC-260 740.5242 1479.0334 19.4 261 CSU/CDC-2611104.0614 2206.1096 15.2 Predicted Chemical Structure Compound (based onMetabolite Predicted accurate Class or MF # Name Formula mass) Pathway 1CSU/CDC-001 C₉H₁₁NO₂ Phenylalanine Phenylalanine metabolism 2CSU/CDC-012 C₁₈H₃₉N — — 3 CSU/CDC-013 C₁₉ H₄₁N — — 4 CSU/CDC-014C₃₃H₃₇N₅O₆ Asp Phe Peptide Arg Tyr (SEQ ID NO: 1) 5 CSU/CDC-019C₁₈H₃₇NO₂ Palmitoyl N-acyl ethanolamide ethanolamine metabolism 6CSU/CDC-039 — — — 7 CSU/CDC-062 C₁₉H₂₃N₅O₃ — — 8 CSU/CDC-066 C₄₂H₃₀N₆O₁₂— — 9 CSU/CDC-067 C₆₂H₁₀₆O₆ TAG(59:7) Triacylglycerol metabolism 10CSU/CDC-072 — — — 11 CSU/CDC-075 — — — 12 CSU/CDC-086 C₄H₈O₅ ThreonateSugar metabolite 13 CSU/CDC-107 — — — 14 CSU/CDC-132 — — — 15CSU/CDC-152 C₃₈H₆₅O₁₀P PG(32:5) Glycerophos- pholipid metabolism 16CSU/CDC-155 C₂₇H₄₀N₄O₄ Gln Leu Peptide Pro Lys (SEQ ID NO: 2) 17CSU/CDC-158 — — — 18 CSU/CDC-164 — — — 19 CSU/CDC-166 C₁₂H₂₀N₄O₇ Glu GlnGly Peptide 20 CSU/CDC-205 C₁₂H₁₆O₅ 3-Carboxy- Fatty acid 4-methyl-5-metabolism propyl-2- furanpropanoic acid (CMPF) 21 CSU/CDC-211C₁₆H₂₉N₇O₇S Arg Asp Peptide Cys Ala (SEQ ID NO: 3) 22 CSU/CDC-212 — — —23 CSU/CDC-213 — — — 24 CSU/CDC-219 — — — 25 CSU/CDC-227 C₂₄H₄₄N₆O₇ GlnVal Leu Peptide Leu Gly (SEQ ID NO: 4) 26 CSU/CDC-229 C₁₈H₃₂O — — 27CSU/CDC-235 C₉H₁₉NOS 8-Methyl- 2-oxocarbo thiooctanal xylic acid doximemetabolism 28 CSU/CDC-238 C₂₄H₄₇NO₂ Erucicoyl N-acyl ethanolamideethanolamine metabolism 29 CSU/CDC-244 C₃₁ H₄₀O₄ Lys Lys Peptide Thr Thr(SEQ ID NO: 5) 30 CSU/CDC-248 — — — 31 CSU/CDC-253 C₁₉H₃₅NO₄ — — 32CSU/CDC-254 — — — 33 CSU/CDC-258 C₂₃H₃₉O₇P Lyso PA(20:4) Glycerohos-pholipid metabolism 34 CSU/CDC-002 C₁₂H₁₄O₅ Trans-2,3,4- Phenylpro-trimethoxy- panoid and cinnamate polyketide metabolism 35 CSU/CDC-028C₁₉H₃₂O₈ Methyl Fatty acid 10,12,13,15- metabolism bisepidioxy-16-hydroper- oxy-8E- octadecenoate 36 CSU/CDC-182 C₁₆H₂₈O₄ — — 37CSU/CDC-204 C₁₅H₂₂N₂O₃ Phe Leu Dipeptide 38 CSU/CDC-247 — — — 39CSU/CDC-003 — — — 40 CSU/CDC-004 C₁₀H₁₂O₃ 5′-(3′-Methoxy- Endogenous4′-hydroxy- metabolite phenyl)- associated gamma- with valerolactonemicrobiome 41 CSU/CDC-006 C₁₇H₁₉NO₃ Piperine Alkaloid metabolism 42CSU/CDC-008 C₂₅H₃₄O₈ Ala Lys Peptide Met Asn (SEQ ID NO: 6) 43CSU/CDC-009 C₁₆H₃₅N — — 44 CSU/CDC-017 — — — 45 CSU/CDC-026 C₃₅H₅₂O₅Furohyperforin Endogenous metabolite - derived from food 46 CSU/CDC-030— — — 47 CSU/CDC-042 — — — 48 CSU/CDC-052 — — — 49 CSU/CDC-061 — — — 50CSU/CDC-070 C₁₉H₃₂O₈ — — 51 CSU/CDC-074 — — — 52 CSU/CDC-083 — — — 53CSU/CDC-084 C₂₃H₂₈N₆O₉ Asp His Peptide Phe Asp (SEQ ID NO: 7) 54CSU/CDC-087 C₂₆H₄₃NO₆ Glycocholic Bile acid acid metabolism 55CSU/CDC-091 — — — 56 CSU/CDC-095 C₉H₁₆O₅ — — 57 CSU/CDC-098 C₁₀H₁₄O₃ — —58 CSU/CDC-099 C₂₆H₄₁N₃O₅ — — 59 CSU/CDC-112 C₁₁H₁₈O₄ alpha- EndogenousCarboxy- metabolite - delta- derived from decalactone food 60CSU/CDC-115 C₂₀H₃₀N₄O₁₂ Poly-g-D- Poly D- glutamate glutamate metabolism61 CSU/CDC-128 — — — 62 CSU/CDC-133 C₁₆H₂₈O₄ — — 63 CSU/CDC-134 C₂₀H₂₀O₆Xanthoxylol Endogenous metabolite - derived from food 64 CSU/CDC-136C₁₆H₂₆O₅ Tetranor-PGE1 Prostaglandin metabolism 65 CSU/CDC-137 — — — 66CSU/CDC-138 — — — 67 CSU/CDC-141 C₁₈H₃₄O₅ 11,12,13- Fatty acidtrihydroxy-9- metabolism octadecenoic acid 68 CSU/CDC-144 C₂₇H₄₆N₆O₈ LeuLys Glu Peptide Pro Pro (SEQ ID NO: 8) 69 CSU/CDC-157 C₃₄H₆₆NO₈PPE(29:1) Glycerophos- pholipid metabolism 70 CSU/CDC-165 C₄₅H₇₄O₁₅(3b,21b)-12- Endogenous Oleanene- metabolite - 3,21,28-triol derivedfrom 28-[arabinosyl- food (1->3)- arabinosyl- (1->3)- arabinoside] 71CSU/CDC-181 — — — 72 CSU/CDC-183 C₁₆H₂₈O₄ Omphalotin A Endogenousmetabolite - derived from food 73 CSU/CDC-184 C21H4107P Lyso-PA(18:1)Glycerophos- pholipid metabolism 74 CSU/CDC-186 — — — 75 CSU/CDC-188 — —— 76 CSU/CDC-193 — — — 77 CSU/CDC-194 — — — 78 CSU/CDC-203 — — — 79CSU/CDC-206 C₁₂H₂₄N₄O₇ — — 80 CSU/CDC-210 C₈H₉N₅O₂ 6-Amino-9H-Endogenous purine-9- metabolite - propanoic derived from acid food 81CSU/CDC-218 — — — 82 CSU/CDC-222 — — — 83 CSU/CDC-224 — — — 84CSU/CDC-237 C₁₇H₃₅NO₂ Pentadecanoyl N-acyl ethanolamide ethanolaminemetabolism 85 CSU/CDC-245 — — — 86 CSU/CDC-250 C₁₈H₃₅NO₂ 3-Keto-Sphingolipid spingosine metabolism 87 CSU/CDC-252 — — — 88 CSU/CDC-005C₁₂H₁₄O₄ — — 89 CSU/CDC-007 C₁₇H₁₉NO₃ — — 90 CSU/CDC-010 — — — 91CSU/CDC-011 C₂₁H₄₄NO₇P Glycero- N-acyl phospho- ethanolamine N-Palmitoylmetabolism Ethanolamine 92 CSU/CDC-015 C₂₆H₅₂NO₇P PC(18:1) Glycero-phospholipid metabolism 93 CSU/CDC-016 C₂₁H₃₀O₅ 4,5α- Sterol dihydro-metabolism cortisone 94 CSU/CDC-018 — — — 95 CSU/CDC-020 C₁₆H₃₃NOPalmitic amide Primary Fatty Acid Amide Metabolism 96 CSU/CDC-021 — — —97 CSU/CDC-022 — — — 98 CSU/CDC-023 C₁₈H₃₇NO Stearamide Primary FattyAcid Amide Metabolism 99 CSU/CDC-024 C₂₂H₄₃NO 13Z- Primary FattyDocosenamide Acid Amide (Erucamide) Metabolism 100 CSU/CDC-025C₃₈H₇₇N₂O₆P SM(d18:1- Sphingolipid 15:0)/SM metabolism (d18:1/14:1- OH)101 CSU/CDC-027 C₂₅H₃₇NO₅ Ala Ile Lys Peptide Thr (SEQ ID NO: 9) 102CSU/CDC-029 C₁₆H₂₈N₆O₅ Lys His Thr Peptides 103 CSU/CDC-031 C₄₆H₈₉NO₁₂SC22-OH Sphingolipid Sulfatide metabolism 104 CSU/CDC-032 C₂₄H₄₀O₈2-glyceryl-6- Prostaglandin keto-PGF1α metabolism 105 CSU/CDC-033C₄₄H₈₅NO₁₁S C20 Sulfatide Sphingolipid metabolism 106 CSU/CDC-034C₄₂H₈₂NO₁₀P PS(36:0) Glycero- phospholipid metabolism 107 CSU/CDC-035 —— — 108 CSU/CDC-036 C₄₃H₈₄NO₁₀P PS(37:0) Glycero- phospholipidmetabolism 109 CSU/CDC-037 C₄₁H₈₀NO₉P PS-O(35:1) Glycero- phospholipidmetabolism 110 CSU/CDC-038 C₃₉H₇₃O₈P PA(36:2) Glycero- phospholipidmetabolism 111 CSU/CDC-040 — — — 112 CSU/CDC-041 — — — 113 CSU/CDC-043C₈₃H₁₅₀O₁₇P₂ CL(74:6) Glycero- phospholipid metabolism 114 CSU/CDC-044C₄₆H₈₉NO₁₁5 C22 Sulfatide Sphingolipid metabolism 115 CSU/CDC-045 — — —116 CSU/CDC-046 — — — 117 CSU/CDC-047 — — — 118 CSU/CDC-048 — — — 119CSU/CDC-049 — — — 120 CSU/CDC-050 C₄₀H₇₅O₈P PA(37:2) Glycero-phospholipid metabolism 121 CSU/CDC-051 — — — 122 CSU/CDC-053 — — — 123CSU/CDC-054 — — — 124 CSU/CDC-055 C₂₀H₃₂N₄O₇ Pro Asp Peptide Pro Leu(SEQ ID NO: 10) 125 CSU/CDC-056 — — — 126 CSU/CDC-057 C831- CL(74:9)Glycero- 1144017P2 phospholipid metabolism 127 CSU/CDC-058 — — — 128CSU/CDC-059 — — — 129 CSU/CDC-060 — — — 130 CSU/CDC-063 C₁₈H₃₄O₂13Z-octa- Fatty acid decenoic acid metabolism 131 CSU/CDC-064 C₁₅H₁₆O₇ —— 132 CSU/CDC-065 C₅₄H₈₈O₂₄ Camellioside D Endogenous metabolite -derived from food 133 CSU/CDC-068 — — — 134 CSU/CDC-069 C₁₅H₂₆N₆O₇ HisSer Lys Peptide 135 CSU/CDC-071 C₁₉H₃₄N₆O₆ Pro Gln Peptide Ala Lys (SEQID NO: 11) 136 CSU/CDC-073 C₄₈H₈₄NO₉P PS-O(42:6) Glycero- phospholipidmetabolism 137 CSU/CDC-076 — — — 138 CSU/CDC-077 — — — 139 CSU/CDC-078 —— — 140 CSU/CDC-079 C₂₁H₃₉O₇P Lyso-PA(18:2) Glycero- phospholipidmetabolism 141 CSU/CDC-080 C₄₅H₈₈NO₁₀P PS(39:0) Glycero- phospholipidmetabolism 142 CSU/CDC-081 — — — 143 CSU/CDC-082 — — — 144 CSU/CDC-085 —— — 145 CSU/CDC-088 — — — 146 CSU/CDC-089 C₂₀H₃₂O₇ Lys His Thr Peptide147 CSU/CDC-090 C16H30N6O6 Lys Gln Gln Peptide 148 CSU/CDC-092 — — — 149CSU/CDC-093 — — — 150 CSU/CDC-094 — — — 151 CSU/CDC-177 C₉H₁₄O₄Nonanedioic Fatty acid Acid metabolism 152 CSU/CDC-097 C₉H₁₂O₃ 2,6-Endogenous Dimethoxy-4- metabolite - methylphenol derived from food 153CSU/CDC-100 C₁₅H₁₇NO₄ — — 154 CSU/CDC-101 C₁₀H₁₂N₅O₅P — — 155CSU/CDC-102 C₁₀H₁₆O₄ Decenedioic Fatty acid acid metabolism 156CSU/CDC-103 C₅H₆O₃ 2-Hydroxy-2,4- Phenylalanine pentadienoate metabolism157 CSU/CDC-104 C₂₄H₂₆O₁₁ — — 158 CSU/CDC-105 C₁₀H₁₈O₅ 3-Hydroxy- Fattyacid sebacic acid metabolism 159 CSU/CDC-106 — — 160 CSU/CDC-108C₁₈H₂₀N₂O₄ Phe Tyr Peptide 161 CSU/CDC-109 — — — 162 CSU/CDC-110 C₉H₁₂O₃2,6- Endogenous Dimethoxy-4- metabolite - methylphenol derived from food163 CSU/CDC-111 C₉H₁₄O₄ — — 164 CSU/CDC-113 C₂₅H₂₂N₄O₆ His Cys PeptideAsp Thr (SEQ ID NO: 12) 165 CSU/CDC-114 C₆H₈O₃ (4E)-2-Oxo- Fatty acidhexenoic acid metabolism 166 CSU/CDC-116 C₇H₈O₂ 4-Methyl- Catecholcatechol metabolism 167 CSU/CDC-117 C₁₂H₂₂O₅ 3-Hydroxy- Fatty aciddodecanedioic metabolism acid 168 CSU/CDC-118 C₂₁H₃₆N₆O₉ Gln Glu GlnPeptide Ile (SEQ ID NO: 13) 169 CSU/CDC-119 C₁₆H₁₂O₆ ChrysoeriolEndogenous metabolite - derived from food 170 CSU/CDC-120 C₁₆H₂₄N₄O₂ — —171 CSU/CDC-121 C₁₉H₃₀O₈ Citroside A Endogenous metabolite - derivedfrom food 172 CSU/CDC-122 — — — 173 CSU/CDC-123 C₄₂H₇₃O₈P PA(39:5)Glycero- phospholipid metabolism 174 CSU/CDC-124 — — — 175 CSU/CDC-125 —— — 176 CSU/CDC-126 — — — 177 CSU/CDC-127 — — — 178 CSU/CDC-129C₉₇H₁₆₇N₅O₄₈ NeuAcalpha2- Sphingolipid 3Galbeta1- metabolism3GalNAcbeta1- 4(9-OAc- NeuAcalpha2- 8NeuAcalpha2- 3)Galbeta1- 4Glcbeta-Cer(d18:1/ 18:0) 179 CSU/CDC-130 — — 180 CSU/CDC-131 — — — 181CSU/CDC-135 C₁₂H₂₄N₆O₄ Arg Ala Ala Peptide 182 CSU/CDC-139 C₁₇H₃₀O₄8E-Heptade- Fatty acid cenedioic acid metabolism 183 CSU/CDC-140C₄₀H₇₈NO₉P PS-O(34:1) Glycero- phospholipid metabolism 184 CSU/CDC-142C₇₉H₁₄0O₁₇P₂ CL(70:7) Glycero- phospholipid metabolism 185 CSU/CDC-143C₃₇H₇₂NO₇P PE-P(32:1) Glycero- phospholipid metabolism 186 CSU/CDC-145 —— — 187 CSU/CDC-146 C₃₅H₄₆O₄ — — 188 CSU/CDC-147 C₃₃H₃₆N₄O₆ 15,16-Dihyd-Bilirubin drobiliverdin breakdown products - Porphyrin metabolism 189CSU/CDC-148 — — — 190 CSU/CDC-149 — — — 191 CSU/CDC-150 — — — 192CSU/CDC-151 — — — 193 CSU/CDC-153 C₄₀H₅₄O₄ Isomytilo- Isoflavinoidxanthin 194 CSU/CDC-154 C₄₃H₇₂NO₈P PE(38:7) Glycero- phospholipidmetabolism 195 CSU/CDC-156 C₄₀H₆₉O₁₀P PG(34:5) Glycero- phospholipidmetabolism 196 CSU/CDC-159 C₂₃H₄₂N₆O₆ Ala Leu Ala Peptide Pro Lys (SEQID NO: 14) 197 CSU/CDC-160 C₄₂H₇₃O₁₀P PG(36:5) Glycero- phospholipidmetabolism 198 CSU/CDC-161 — — — 199 CSU/CDC-162 C₅₃H₇₂O₈ AmitenoneEndogenous metabolite - derived from food 200 CSU/CDC-163 C₂₆H₄₈N₆O₆ LeuAla Pro Peptide Lys Ile (SEQ ID NO: 15) 201 CSU/CDC-167 — — — 202CSU/CDC-168 C₁₈H₁₆O₆ 2-Oxo-3- Phenylalanine phenylpro- metabolism panoicacid 203 CSU/CDC-169 — — — 204 CSU/CDC-170 — — — 205 CSU/CDC-171C₃₇H₆₃O₈P PA(24:5) Glycero- phospholipid metabolism 206 CSU/CDC-172C₅H₈O₄ 2-Acetolactic Pantothenate acid and CoA Biosynthesis Pathway 207CSU/CDC-173 — — — 208 CSU/CDC-174 C₁₀H₂₀N₆O₄ Asn Arg Dipeptide 209CSU/CDC-175 C₆H₁₀O₄ α-Ketopantoic Pantothenate acid and CoA BiosynthesisPathway 210 CSU/CDC-176 C₉H₁₂O₃ Epoxyoxo- Endogenous phoronemetabolite - derived from food 211 CSU/CDC-096 C₉H₁₄O₄ 5-Butyltetrahy-Endogenous dro-2-oxo-3- metabolite - furancarboxylic derived from acidfood 212 CSU/CDC-178 C₉₁₄O₄ 3,6-Nonadienal Endogenous metabolite -derived from food 213 CSU/CDC-179 C₂₆H₄₂O₁₀ Cofaryloside Endogenousmetabolite - derived from food 214 CSU/CDC-180 C₂₅H₄₂N₂O₇S Epidihydro-Endogenous phaseic acid metabolite - derived from food 215 CSU/CDC-185 —— — 216 CSU/CDC-187 — — — 217 CSU/CDC-189 — — — 218 CSU/CDC-190 — — —219 CSU/CDC-191 C₈₁H₁₄4O₁₇P₂ CL(72:7) Glycero- phospholipid metabolism220 CSU/CDC-192 — — — 221 CSU/CDC-195 C₁₇H₂₄O₃ Benzyl- Phenylpro-succinate panoic acid metabolism 222 CSU/CDC-196 — — — 223 CSU/CDC-197 —— — 224 CSU/CDC-198 — — — 225 CSU/CDC-199 — — — 226 CSU/CDC-200 — — —227 CSU/CDC-201 — — — 228 CSU/CDC-202 C₄₂H₆₇O₈P PA(39:8) Glycero-phospholipid metabolism 229 CSU/CDC-207 C₂₀H₄₁NO₂ Stearoyl N-acylethanolamide ethanolamine metabolism 230 CSU/CDC-208 C₅₆H₉₉NO₁₄3-O-acetyl- Sphingolipid sphingosine- metabolism 2,3,4,6-tetra-O-acetyl- GalCer (d18:1/h22:0) 231 CSU/CDC-209 — — — 232 CSU/CDC-214C₁₄H₂₉NO₂ Lauroyl N-acyl ethanolamide ethanolamine metabolism 233CSU/CDC-215 — — — 234 CSU/CDC-216 C₃₁H₄₆O₂ — — 235 CSU/CDC-217 — — — 236CSU/CDC-220 — — — 237 CSU/CDC-221 — — — 238 CSU/CDC-223 — — — 239CSU/CDC-225 — — — 240 CSU/CDC-226 — — — 241 CSU/CDC-228 C₂₃H₄₀O₆ — — 242CSU/CDC-230 C₁₉H₃₆O₂ Methyl oleate Oleic acid ester 243 CSU/CDC-231 — —— 244 CSU/CDC-232 — — — 245 CSU/CDC-233 — — — 246 CSU/CDC-234 C₇H₁₂N₂O₆L-beta- Peptide aspartyl- L-serine 247 CSU/CDC-236 C₁₉H₃₆O₃ 2-oxo-nona-Fatty acid decanoic acid metabolism 248 CSU/CDC-239 C₁₉H₃₈O₃ 2-Hydroxy-Fatty acid nonadecanoic metabolism acid 249 CSU/CDC-240 C₃₀H₄₈O₂4,4-Dimethyl- Sterol 14a- metabolism formyl-5a- cholesta-8,24-dien-3b-ol 250 CSU/CDC-241 C₃₀H48O Butyro- Sterol spermone metabolism251 CSU/CDC-242 C₂₂H₄₅NO₂ Eicosanoyl N-acyl ethanolamide ethanolaminemetabolism 252 CSU/CDC-243 C₂₂H₄₂O₄ — — 253 CSU/CDC-246 C₇H₅NS₂ — — 254CSU/CDC-249 C₄₇H₈₀O₂ Cholesterol Sterol ester (20:2) metabolism 255CSU/CDC-251 C₂₆H₃₇NO₆ — — 256 CSU/CDC-255 — — — 257 CSU/CDC-256 — — —258 CSU/CDC-257 — — — 259 CSU/CDC-259 — — — 260 CSU/CDC-260 C₈₃H₁₄₈O₁₇P₂CL(74:7) Glycero- phospholipid metabolism 261 CSU/CDC-261 — — —

wherein each molecular feature is identified by its mass to chargeratio; and (iv) inputting the abundance values from step (iii) into aclassification model trained with samples of metabolite extracts derivedfrom suitable controls, wherein if the molecular features of Table A orTable C are provided, the classification model is Least AbsoluteShrinkage and Selection Operator (LASSO) and if the molecular featuresof Table D are provided, the classification model is Random Forest (RF),and wherein the classification model produces a disease score and thedisease score distinguishes subjects with Lyme disease.
 2. The method ofclaim 1, wherein the subject is diagnosed with Stage 1 Lyme disease.